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class=\"jet-listing-grid__item jet-listing-dynamic-post-18284 jet-equal-columns elementor-dcss-3610932805797\" data-post-id=\"18284\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div 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data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/agentic-ai\/\" class=\"jet-listing-dynamic-terms__link\">Agentic AI<\/a><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/human-centered-ai\/\" class=\"jet-listing-dynamic-terms__link\">Human Centered AI<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 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href=\"https:\/\/megagon.ai\/jp\/publications\/steering-human-llm-co-planning-multi-agent-systems\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 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display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" >Zeyu He, <a class=\"publication-author\"  title=\"Click to check Hannah Kim's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/hannah-kim\/\" target=\"_blank\">Hannah Kim<\/a>, <a class=\"publication-author\"  title=\"Click to check Dan Zhang's profile page.\" href=\"https:\/\/megagon.ai\/jp\/our-team\/dan-zhang\/\" target=\"_blank\">Dan Zhang<\/a>, <a class=\"publication-author\"  title=\"Click to check Estevam Hruschka's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/estevam-hruschka\/\" target=\"_blank\">Estevam Hruschka<\/a><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_18284\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIn orchestrated multi-agent systems, humans often struggle to manage plans due to their complexity and limited transparency. Existing approaches rely on outcome-level supervision, where users verify only final outputs without visibility into intermediate reasoning. We formalize a design space for human-LLM co-planning interactions along three axes: mode (semantic vs. structural), scope (global vs. targeted), and level (low- vs. high-level edits). We realize it in AMBIPOM, a prototype supporting process-level supervision through both semantic and structural interactions. Through a user study, we characterize how users navigate this space, revealing hybrid workflows and effort-control-risk trade-offs; through a controlled benchmark, we analyze how LLMs revise plans under varying scope and revision strategies. Our findings yield design insights for more transparent, controllable, and effective human-AI co-planning. We release code and data at https:\/\/github.com\/megagonlabs\/ambipom.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c41ed e-con-full mt-auto e-flex e-con e-child\" data-id=\"f3c41ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0da405f e-con-full e-flex e-con e-child\" data-id=\"0da405f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139aadf btn-light btn-gray elementor-align-center elementor-widget__width-auto jedv-enabled--yes elementor-invisible elementor-widget elementor-widget-button\" data-id=\"139aadf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/megagon.ai\/jp\/publications\/steering-human-llm-co-planning-multi-agent-systems\/\" target=\"_blank\" title=\"Click to read the publication.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u3082\u3063\u3068\u8aad\u3080<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edd938e e-con-full e-flex e-con e-child\" data-id=\"edd938e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6449dd3 e-con-full e-flex e-con e-child\" data-id=\"6449dd3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c006478 jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"c006478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .excerpt .elementor-widget-container {\n    display: -webkit-box;\n    -webkit-line-clamp: 3; \/* Number of lines to show *\/\n    -webkit-box-orient: vertical;\n    overflow: hidden;\n    text-overflow: ellipsis;\n}\n\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div><div class=\"jet-listing-grid__item jet-listing-dynamic-post-17751 jet-equal-columns elementor-dcss-3611271217739\" data-post-id=\"17751\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b193990 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"b193990\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">ACL<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff1bfb elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"6ff1bfb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">2026<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cf49db9 e-con-full e-flex e-con e-child\" data-id=\"cf49db9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/nlp-llms\/\" class=\"jet-listing-dynamic-terms__link\">NLP &amp; LLMs<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 elementor-widget elementor-widget-heading\" data-id=\"aa766a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/arxiv.org\/pdf\/2511.10899\" target=\"_blank\" title=\"Click to open publication\">From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e11c linkIcon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f21e11c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<a class=\"elementor-icon elementor-animation-grow\" href=\"https:\/\/megagon.ai\/jp\/publications\/tool-induced-myopia-in-llms\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z\"><\/path><\/svg>\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428c8d8 elementor-widget elementor-widget-jet-listing-dynamic-field\" data-id=\"428c8d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-field.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-field display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" ><a class=\"publication-author\"  title=\"Click to check Farima Fatahi Bayat's profile page.\" href=\"https:\/\/megagon.ai\/jp\/our-team\/farima-fatahi\/\" target=\"_blank\">Farima Fatahi Bayat<\/a>, <a class=\"publication-author\"  title=\"Click to check Pouya Pezeshkpour's profile page.\" href=\"https:\/\/megagon.ai\/jp\/our-team\/pouya-pezeshkpour\/\" target=\"_blank\">Pouya Pezeshkpour<\/a>, <a class=\"publication-author\"  title=\"Click to check Estevam Hruschka's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/estevam-hruschka\/\" target=\"_blank\">Estevam Hruschka<\/a><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_17751\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tTool-augmented Language Models (TaLMs)\r\ncan invoke external tools to solve problems\r\nbeyond their parametric capacity. However,\r\nit remains unclear whether these tool-enabled\r\ngains reflect trustworthy reasoning. Focusing\r\non the Code Interpreter tool, we show that even\r\nwhen tools are selected and executed correctly,\r\nTaLMs treat tool outputs as substitutes for reasoning, producing solutions that appear correct\r\nbut lack coherent justification. We term this failure mode Tool-Induced Myopia (TIM), and\r\nstudy it using PYMATH, a benchmark of 1,679\r\ncompetition-level mathematical problems for\r\nwhich Python code is helpful but not sufficient.\r\nWe further develop a multi-dimensional evaluation suite to quantify reasoning degradation\r\nin TaLMs relative to their non-tool counterparts. Our findings reveal that while TaLMs\r\nachieve up to a 19.3 percentage point gain in\r\nfinal-answer accuracy, their reasoning behavior\r\nconsistently deteriorates (e.g., non-tool LLMs\r\nwin up to 41.5% more often in pairwise comparisons of reasoning process). This degradation\r\nintensifies with tool use; the more frequently a\r\nmodel invokes tools, the less coherent its reasoning becomes. Moreover, tool use shifts errors from arithmetic mistakes toward global reasoning failures (logic, assumption, creativity);\r\nwith TIM present in ~55% of high-risk cases.\r\nFinally, we propose a preference-optimizationbased framework that realigns TaLMs to use\r\ntools as assistive evidence, improving both\r\nfinal-answer accuracy and reasoning depth under tool use. Codes and data are available at:\r\nhttps:\/\/github.com\/megagonlabs\/TIM.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c41ed e-con-full mt-auto e-flex e-con e-child\" data-id=\"f3c41ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0da405f e-con-full e-flex e-con e-child\" data-id=\"0da405f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139aadf btn-light btn-gray elementor-align-center elementor-widget__width-auto jedv-enabled--yes elementor-invisible elementor-widget elementor-widget-button\" data-id=\"139aadf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/megagon.ai\/jp\/publications\/tool-induced-myopia-in-llms\/\" target=\"_blank\" title=\"Click to read the publication.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u3082\u3063\u3068\u8aad\u3080<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edd938e e-con-full e-flex e-con e-child\" data-id=\"edd938e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6449dd3 e-con-full e-flex e-con e-child\" data-id=\"6449dd3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c006478 jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"c006478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .excerpt .elementor-widget-container {\n    display: -webkit-box;\n    -webkit-line-clamp: 3; \/* Number of lines to show *\/\n    -webkit-box-orient: vertical;\n    overflow: hidden;\n    text-overflow: ellipsis;\n}\n\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div><div class=\"jet-listing-grid__item jet-listing-dynamic-post-17780 jet-equal-columns elementor-dcss-3611602656952\" data-post-id=\"17780\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b193990 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"b193990\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">ACL Demo<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff1bfb elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"6ff1bfb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">2026<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cf49db9 e-con-full e-flex e-con e-child\" data-id=\"cf49db9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/publications-tags\/data-ai-symbiosis\/\" class=\"jet-listing-dynamic-terms__link\">Data AI Symbiosis<\/a><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/llm-nlp-jp\/\" class=\"jet-listing-dynamic-terms__link\">LLM &amp; NLP<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 elementor-widget elementor-widget-heading\" data-id=\"aa766a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/arxiv.org\/abs\/2603.06915\" target=\"_blank\" title=\"Click to open publication\">A Dynamic Self-Evolving Extraction System<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e11c linkIcon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f21e11c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<a class=\"elementor-icon elementor-animation-grow\" href=\"https:\/\/megagon.ai\/jp\/publications\/dynamic-self-evolving-extraction-system\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z\"><\/path><\/svg>\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428c8d8 elementor-widget elementor-widget-jet-listing-dynamic-field\" data-id=\"428c8d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-field.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-field display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" >Moin Amin-Naseri, <a class=\"publication-author\"  title=\"Click to check Hannah Kim's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/hannah-kim\/\" target=\"_blank\">Hannah Kim<\/a>, <a class=\"publication-author\"  title=\"Click to check Estevam Hruschka's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/estevam-hruschka\/\" target=\"_blank\">Estevam Hruschka<\/a><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_17780\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation. High-quality extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers. In many domains&#8211;such as medical, legal, and HR&#8211;the extraction model must also adapt to shifting terminology and benefit from explicit reasoning over structured knowledge. We propose DySECT, a Dynamic Self-Evolving Extraction and Curation Toolkit, which continually improves as it is used. The system incrementally populates a versatile, self-expanding knowledge base (KB) with triples extracted by the LLM. The KB further enriches itself through the integration of probabilistic knowledge and graph-based reasoning, gradually accumulating domain concepts and relationships. The enriched KB then feeds back into the LLM extractor via prompt tuning, sampling of relevant few-shot examples, or fine-tuning using KB-derived synthetic data. As a result, the system forms a symbiotic closed-loop cycle in which extraction continuously improves knowledge, and knowledge continuously improves extraction.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c41ed e-con-full mt-auto e-flex e-con e-child\" data-id=\"f3c41ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0da405f e-con-full e-flex e-con e-child\" data-id=\"0da405f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139aadf btn-light btn-gray elementor-align-center elementor-widget__width-auto jedv-enabled--yes elementor-invisible elementor-widget elementor-widget-button\" data-id=\"139aadf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/megagon.ai\/jp\/publications\/dynamic-self-evolving-extraction-system\/\" target=\"_blank\" title=\"Click to read the publication.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u3082\u3063\u3068\u8aad\u3080<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edd938e e-con-full e-flex e-con e-child\" data-id=\"edd938e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f7ea839 elementor-align-center iconBtn jedv-enabled--yes elementor-widget elementor-widget-button\" data-id=\"f7ea839\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/github.com\/megagonlabs\/dysect\" target=\"_blank\" title=\"Click to view Github.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M15 21C15 21 15 18.73 15 18C15 17.37 15.15 16.04 14.5 15.5C15.89 15.37 16.98 14.92 18 14C19.02 13.08 19.5 11.69 19.5 9.5C19.5 8 19.25 7 18.5 6C18.79 5.22 18.84 4 18.5 3C16.94 3 15.53 4.07 15 4.5C14.61 4.4 13.67 4 12 4C10.33 4 9.39 4.4 9 4.5C8.47 4.07 7.06 3 5.5 3C5.16 4 5.21 5.22 5.5 6C4.75 7 4.5 8 4.5 9.5C4.5 11.69 4.98 13.08 6 14C7.02 14.92 8.11 15.37 9.5 15.5C8.85 16.04 9 17.37 9 18C9 18.73 9 21 9 21\" stroke=\"#15151E\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M9 19C7.59 19 6.16 18.44 5.31 17.81C4.47 17.18 4.22 16.15 3 15.5\" stroke=\"#15151E\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-051ac8b jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"051ac8b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"tooltip-container\">\r\n    <div class=\"tooltip-content\">\u30ae\u30ba\u30d6<\/div>\r\n<\/div>\r\n\r\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6449dd3 e-con-full e-flex e-con e-child\" data-id=\"6449dd3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c006478 jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"c006478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .excerpt .elementor-widget-container {\n    display: -webkit-box;\n    -webkit-line-clamp: 3; \/* Number of lines to show *\/\n    -webkit-box-orient: vertical;\n    overflow: hidden;\n    text-overflow: ellipsis;\n}\n\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div><div class=\"jet-listing-grid__item jet-listing-dynamic-post-18165 jet-equal-columns elementor-dcss-3611915777720\" data-post-id=\"18165\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b193990 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"b193990\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">LT4HALA<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff1bfb elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"6ff1bfb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">2026<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cf49db9 e-con-full e-flex e-con e-child\" data-id=\"cf49db9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/llm-nlp-jp\/\" class=\"jet-listing-dynamic-terms__link\">LLM &amp; NLP<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 elementor-widget elementor-widget-heading\" data-id=\"aa766a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/megagon.ai\/wp-content\/uploads\/2026\/06\/Extending-omnes-flores-for-the-EvaLatin-2026-Dependency-Parsing-Tasks.pdf\" target=\"_blank\" title=\"Click to open publication\">Extending omnes flores for the EvaLatin 2026 Dependency Parsing Tasks<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e11c linkIcon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f21e11c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<a class=\"elementor-icon elementor-animation-grow\" href=\"https:\/\/megagon.ai\/jp\/publications\/extending-omnes-flores-for-the-evalatin-2026-dependency-parsing-tasks\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z\"><\/path><\/svg>\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428c8d8 elementor-widget elementor-widget-jet-listing-dynamic-field\" data-id=\"428c8d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-field.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-field display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" ><a class=\"publication-author\"  title=\"Click to check Hiroshi Matsuda's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/hiroshi\/\" target=\"_blank\">Hiroshi Matsuda<\/a>, Masayuki Asahara<\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_18165\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tomnes flores is an NLP framework based on Universal Dependencies (UD) that utilizes multilingual Large Language Models (LLMs), and its default model is trained on data from 40 UD languages comprising 40 treebanks. For the EvaLatin 2026 Dependency Parsing Tasks, we extended the training data of omnes flores by incorporating\r\nsix public Latin treebanks from UD and trained a dependency parsing model using the extended training data. The dependency parser of omnes flores normally takes a list of word FORM values as input. However, since the EvaLatin 2026 test data includes an UPOS column, we investigated whether incorporating both FORM and UPOS\r\nduring both training and inference could improve parsing accuracy. Our experiments show that training using both FORM and UPOS improves performance by 0.5-1.0 LAS points on Prose compared with training using only FORM, but decreases performance by 5 points on Poetry.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c41ed e-con-full mt-auto e-flex e-con e-child\" data-id=\"f3c41ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0da405f e-con-full e-flex e-con e-child\" data-id=\"0da405f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139aadf btn-light btn-gray elementor-align-center elementor-widget__width-auto jedv-enabled--yes elementor-invisible elementor-widget elementor-widget-button\" data-id=\"139aadf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/megagon.ai\/jp\/publications\/extending-omnes-flores-for-the-evalatin-2026-dependency-parsing-tasks\/\" target=\"_blank\" title=\"Click to read the publication.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u3082\u3063\u3068\u8aad\u3080<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edd938e e-con-full e-flex e-con e-child\" data-id=\"edd938e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6449dd3 e-con-full e-flex e-con e-child\" data-id=\"6449dd3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c006478 jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"c006478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .excerpt .elementor-widget-container {\n    display: -webkit-box;\n    -webkit-line-clamp: 3; \/* Number of lines to show *\/\n    -webkit-box-orient: vertical;\n    overflow: hidden;\n    text-overflow: ellipsis;\n}\n\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div><div class=\"jet-listing-grid__item jet-listing-dynamic-post-18163 jet-equal-columns elementor-dcss-3612248236924\" data-post-id=\"18163\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b193990 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"b193990\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">UDW<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff1bfb elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"6ff1bfb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">2026<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cf49db9 e-con-full e-flex e-con e-child\" data-id=\"cf49db9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/llm-nlp-jp\/\" class=\"jet-listing-dynamic-terms__link\">LLM &amp; NLP<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 elementor-widget elementor-widget-heading\" data-id=\"aa766a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/megagon.ai\/wp-content\/uploads\/2026\/06\/Probing-the-Dynamics-of-Syntactic-Ability-Acquisition-Throughout-LLM-Pretraining.pdf\" target=\"_blank\" title=\"Click to open publication\">Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e11c linkIcon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f21e11c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<a class=\"elementor-icon elementor-animation-grow\" href=\"https:\/\/megagon.ai\/jp\/publications\/probing-the-dynamics-of-syntactic-ability-acquisition-throughout-llm-pretraining\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z\"><\/path><\/svg>\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428c8d8 elementor-widget elementor-widget-jet-listing-dynamic-field\" data-id=\"428c8d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-field.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-field display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" ><a class=\"publication-author\"  title=\"Click to check Hiroshi Matsuda's profile page.\" href=\"https:\/\/megagon.ai\/our-team\/hiroshi\/\" target=\"_blank\">Hiroshi Matsuda<\/a>, Masayuki Asahara<\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_18163\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIn this research, we introduce LoRA probing, a lightweight approach for observing how core syntactic abilities emergeduring LLM pretraining. Leveraging OLMo-2\u2019s public intermediate checkpoints, we trace learning curves across 24 pretraining stages on 33 Universal Dependencies languages by fine-tuning LoRA with step-by-step parsing\r\ninstructions and a simple tabular output. To fit the relatively short context length of the OLMo-2, we design a compact 2-step-no-form prompt template and this matches the baseline in average accuracy while halving the context length and substantially increasing throughput, enabling efficient large-scale evaluation. Token Recall surpasses 0.9 within the first 1\u20132K pretraining steps, indicating that stable output formatting emerges early. Despite OLMo-2-7B\u2019s English-centric pretraining, LAS exceeds 80 points in 29 of 33 languages; however, relations such as iobj and csubj show delayed onset and instability across many languages. LoRA probing thus provides a practical, reproducible lens on the cross-lingual dynamics of syntactic acquisition during LLM pretraining.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c41ed e-con-full mt-auto e-flex e-con e-child\" data-id=\"f3c41ed\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-0da405f e-con-full e-flex e-con e-child\" data-id=\"0da405f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-139aadf btn-light btn-gray elementor-align-center elementor-widget__width-auto jedv-enabled--yes elementor-invisible elementor-widget elementor-widget-button\" data-id=\"139aadf\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;zoomIn&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/megagon.ai\/jp\/publications\/probing-the-dynamics-of-syntactic-ability-acquisition-throughout-llm-pretraining\/\" target=\"_blank\" title=\"Click to read the publication.\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">\u3082\u3063\u3068\u8aad\u3080<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-edd938e e-con-full e-flex e-con e-child\" data-id=\"edd938e\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6449dd3 e-con-full e-flex e-con e-child\" data-id=\"6449dd3\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c006478 jedv-enabled--yes elementor-widget elementor-widget-html\" data-id=\"c006478\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .excerpt .elementor-widget-container {\n    display: -webkit-box;\n    -webkit-line-clamp: 3; \/* Number of lines to show *\/\n    -webkit-box-orient: vertical;\n    overflow: hidden;\n    text-overflow: ellipsis;\n}\n\n<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div><div class=\"jet-listing-grid__item jet-listing-dynamic-post-18151 jet-equal-columns elementor-dcss-3612581155195\" data-post-id=\"18151\"  >\t\t<div data-elementor-type=\"jet-listing-items\" data-elementor-id=\"14305\" class=\"elementor elementor-14305 elementor-1978 elementor-1978\" data-elementor-post-type=\"jet-engine\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2075ce publication e-flex e-con-boxed e-con e-parent\" data-id=\"e2075ce\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-28dbc33 e-con-full e-flex e-con e-child\" data-id=\"28dbc33\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-11b8f9d e-con-full e-flex e-con e-child\" data-id=\"11b8f9d\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b193990 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"b193990\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff1bfb elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"6ff1bfb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\">2026<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cf49db9 e-con-full e-flex e-con e-child\" data-id=\"cf49db9\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09a7261 publicationTag elementor-widget elementor-widget-jet-listing-dynamic-terms\" data-id=\"09a7261\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-terms.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-terms\"><a href=\"https:\/\/megagon.ai\/jp\/publications-tags\/llm-nlp-jp\/\" class=\"jet-listing-dynamic-terms__link\">LLM &amp; NLP<\/a><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ac1e33b e-con-full e-flex e-con e-child\" data-id=\"ac1e33b\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa766a2 elementor-widget elementor-widget-heading\" data-id=\"aa766a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<span class=\"elementor-heading-title elementor-size-default\"><a href=\"https:\/\/megagon.ai\/wp-content\/uploads\/2026\/06\/Social-Bias-Evaluation-via-Localization-Grounded-in-Laws-and-Regulations.pdf\" target=\"_blank\" title=\"Click to open publication\">\u6cd5\u4ee4\u306b\u57fa\u3065\u304f\u30ed\u30fc\u30ab\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308b\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u8a55\u4fa1<\/a><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f21e11c linkIcon elementor-view-default elementor-widget elementor-widget-icon\" data-id=\"f21e11c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-wrapper\">\n\t\t\t<a class=\"elementor-icon elementor-animation-grow\" href=\"https:\/\/megagon.ai\/jp\/publications\/social-bias-evaluation-via-localization-grounded-in-laws-and-regulations\/\" target=\"_blank\">\n\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-link\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z\"><\/path><\/svg>\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-428c8d8 elementor-widget elementor-widget-jet-listing-dynamic-field\" data-id=\"428c8d8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"jet-listing-dynamic-field.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"jet-listing jet-listing-dynamic-field display-inline\"><div class=\"jet-listing-dynamic-field__inline-wrap\"><div class=\"jet-listing-dynamic-field__content\" >\u91d1\u5b50 \u6b63\u5f18, <a class=\"publication-author\"  title=\"Click to check \u677e\u7530 \u5bdb's profile page.\" href=\"https:\/\/megagon.ai\/jp\/our-team\/hiroshi-matsuda\/\" target=\"_blank\">\u677e\u7530 \u5bdb<\/a>, \u9234\u6728 \u4e45\u7f8e, \u95a2\u6839 \u8061<\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-86f7bf5 excerpt elementor-widget elementor-widget-text-editor\" data-id=\"86f7bf5\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"excerpt_18151\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\u5927\u898f\u6a21\u8a00\u8a9e\u30e2\u30c7\u30eb\u306f\u5dee\u5225\u7684\u306a\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u3092\u542b\u3080\u60c5\u5831\u3092\u751f\u6210\u3059\u308b\u30ea\u30b9\u30af\u304c\u3042\u308a\uff0c\u305d\u306e\u8a55\u4fa1\u304c\u5fc5\u8981\u3067\u3042\u308b\uff0e\u3057\u304b\u3057\uff0c\u4f55\u3092\u300c\u5dee\u5225\u7684\u306a\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u300d\u3068\u307f\u306a\u3059\u304b\u306f\u793e\u4f1a\u7684\u6587\u8108\u306b\u4f9d\u5b58\u3059\u308b\u305f\u3081\uff0c\u666e\u904d\u7684\u306a\u4fa1\u5024\u57fa\u6e96\u3092\u5b9a\u7fa9\u3059\u308b\u3053\u3068\u306f\u96e3\u3057\u304f\uff0c\u500b\u3005\u306e\u793e\u4f1a\u7684\u6587\u8108\u306b\u304a\u3044\u3066\u5408\u610f\u53ef\u80fd\u306a\u4fa1\u5024\u57fa\u6e96\u306b\u57fa\u3065\u3044\u305f\u5b89\u5168\u6027\u62c5\u4fdd\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308b\uff0e\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u69cb\u7bc9\u306b\u95a2\u3059\u308b\u5148\u884c\u7814\u7a76\u3067\u306f\uff0c\u793e\u4f1a\u7684\u6587\u8108\u306e\u4e00\u3064\u3067\u3042\u308b\u56fd\u306b\u3088\u308b\u4fa1\u5024\u57fa\u6e96\u306e\u76f8\u9055\u306b\u5bfe\u5fdc\u3059\u308b\u305f\u3081\uff0c\u5f53\u8a72\u56fd\u306e\u30a2\u30ce\u30c6\u30fc\u30bf\u30fc\u3092\u7528\u3044\u3066\u30ed\u30fc\u30ab\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u3092\u884c\u3063\u3066\u3044\u308b\u304c\uff0c\u3053\u306e\u624b\u6cd5\u306f\u30a2\u30ce\u30c6\u30fc\u30bf\u30fc\u306e\u4e3b\u89b3\u306b\u5f37\u304f\u4f9d\u5b58\u3057\u3066\u304a\u308a\uff0c\u5b89\u5168\u6027\u306e\u5224\u65ad\u304c\u5f53\u8a72\u56fd\u306b\u304a\u3044\u3066\u5408\u610f\u53ef\u80fd\u306a\u3082\u306e\u3067\u3042\u308b\u3053\u3068\u3092\u660e\u78ba\u306b\u306f\u62c5\u4fdd\u3057\u3066\u3044\u306a\u3044\uff0e\u672c\u7814\u7a76\u3067\u306f\uff0c\u5404\u56fd\u306b\u304a\u3044\u3066\u5408\u610f\u3055\u308c\u305f\u300c\u5dee\u5225\u7684\u306a\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u300d\u306e\u6700\u4f4e\u9650\u306e\u57fa\u6e96\u3068\u3057\u3066\uff0c\u6cd5\u4ee4\u3068\u305d\u306e\u5224\u4f8b\u7b49\u3092\u6839\u62e0\u3068\u3059\u308b\u5b89\u5168\u6027\u62c5\u4fdd\u306e\u30ed\u30fc\u30ab\u30e9\u30a4\u30bc\u30fc\u30b7\u30e7\u30f3\u3092\u63d0\u6848\u3057\uff0c\u65e5\u672c\u306e\u96c7\u7528\u95a2\u9023\u9818\u57df\u304a\u3088\u3073\u533b\u7642\u63d0\u4f9b\u95a2\u9023\u9818\u57df\u306e\u6cd5\u4ee4\u306b\u304a\u3044\u3066\u5dee\u5225\u3068\u5224\u65ad\u3055\u308c\u305f\u4e8b\u4f8b\u3092\u53ce\u96c6\u3057\u3066\uff0c\u793e\u4f1a\u7684\u30d0\u30a4\u30a2\u30b9\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8 &#8211; JLawBias \u3092\u69cb\u7bc9\u3057\uff0c6 \u3064\u306e\u65e5\u672c\u8a9e\u5bfe\u5fdc LLM \u306b\u5bfe\u3057\u3066\u7c21\u6613\u306a\u8a55\u4fa1\u3092\u5b9f\u65bd\u3057\u3066\u624b\u6cd5\u306e\u6709\u52b9\u6027\u3092\u78ba\u8a8d\u3057\u305f\uff0e\r\n\r\n\u3053\u3053\u306b\u63b2\u8f09\u3057\u305f\u8457\u4f5c\u7269\u306e\u5229\u7528\u306b\u95a2\u3059\u308b\u6ce8\u610f \u672c\u8457\u4f5c\u7269\u306e\u8457\u4f5c\u6a29\u306f\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u306b\u5e30\u5c5e\u3057\u307e\u3059\u3002\u672c\u8457\u4f5c\u7269\u306f\u8457\u4f5c\u6a29\u8005\u3067\u3042\u308b\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u306e\u8a31\u53ef\u306e\u3082\u3068\u306b\u63b2\u8f09\u3059\u308b\u3082\u306e\u3067\u3059\u3002\u3054\u5229\u7528\u306b\u5f53\u305f\u3063\u3066\u306f\u300c\u8457\u4f5c\u6a29\u6cd5\u300d\u306b\u5f93\u3046\u3053\u3068\u3092\u304a\u9858\u3044\u3044\u305f\u3057\u307e\u3059\u3002\r\nNotice for the use of this material. 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