FacTool: Factuality Detection in Generative AI - A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios

I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu

Primary Area: general machine learning (i.e., none of the above)

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Keywords: Factuality, LLM

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TL;DR: We introduce FacTool, a tool augmented factuality detection framework that can effectively detect diverse factual errors generated by LLMs..

Abstract: The emergence of generative pre-trained models has facilitated the synthesis of high-quality text but has also posed challenges in identifying factual errors in the generated text. In particular: (1) A wider range of tasks now face an increasing risk of containing factual errors when handled by generative models. (2) The content generated by these models tends to be lengthy and lacks clearly defined granularity for individual facts. (3) There is a scarcity of explicit evidence available during the process of fact checking. With the above challenges in mind, in this paper, we propose FacTool, a tool augmented multi-task and multi-domain framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Experiments on four different tasks (knowledge-based QA, code generation, mathematical reasoning, and scientific literature review) show the efficacy of the proposed method.

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Submission Number: 3682