人間中心のAI

Large language models (LLMs) are being increasingly used for planning in orchestrated multi-agent systems. However, existing LLM-based approaches often fall short of human expectations and, critically, lack effective mechanisms for users to inspect, understand, and control their behaviors. These limitations call for enhanced transparency, controllability, and human oversight. To address this, we introduce AIPOM, a system supporting human-in-the-loop planning through conversational and graph-based interfaces. AIPOM enables users to transparently inspect, refine, and collaboratively guide LLM-generated plans, significantly enhancing user control and trust in multi-agent workflows.

Large language models (LLMs) have shown remarkable performance across various natural language processing (NLP) tasks, indicating their significant potential as data annotators. Although LLM-generated annotations are more cost-effective and efficient to obtain, they are often erroneous for complex or domain-specific tasks and may introduce bias when compared to human annotations. Therefore, instead of completely replacing human annotators with LLMs, we need to leverage the strengths of both LLMs and humans to ensure the accuracy and reliability of annotations. This paper presents a multi-step human-LLM collaborative approach where (1) LLMs generate labels and provide explanations, (2) a verifier assesses the quality of LLM-generated labels, and (3) human annotators re-annotate a subset of labels with lower verification scores. To facilitate human-LLM collaboration, we make use of LLM’s ability to rationalize its decisions. LLM-generated explanations can provide additional information to the verifier model as well as help humans better understand LLM labels. We demonstrate that our verifier is able to identify potentially incorrect LLM labels for human re-annotation. Furthermore, we investigate the impact of presenting LLM labels and explanations on human re-annotation through crowdsourced studies.

We present MEGAnno, a novel exploratory annotation framework designed for NLP researchers and practitioners. Unlike existing labeling tools that focus on data labeling only, our framework aims to support a broader, iterative ML workflow including data exploration and model development. With MEGAnno’s API, users can programmatically explore the data through sophisticated search and automated suggestion functions and incrementally update task schema as their project evolve. Combined with our widget, the users can interactively sort, filter, and assign labels to multiple items simultaneously in the same notebook where the rest of the NLP project resides. We demonstrate MEGAnno’s flexible, exploratory, efficient, and seamless labeling experience through a sentiment analysis use case.