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Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector

Authors :
Cheng, Xiaoxue
Li, Junyi
Zhao, Wayne Xin
Zhang, Hongzhi
Zhang, Fuzheng
Zhang, Di
Gai, Kun
Wen, Ji-Rong
Publication Year :
2024

Abstract

Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we propose an autonomous LLM-based agent framework, called HaluAgent, which enables relatively smaller LLMs (e.g. Baichuan2-Chat 7B) to actively select suitable tools for detecting multiple hallucination types such as text, code, and mathematical expression. In HaluAgent, we integrate the LLM, multi-functional toolbox, and design a fine-grained three-stage detection framework along with memory mechanism. To facilitate the effectiveness of HaluAgent, we leverage existing Chinese and English datasets to synthesize detection trajectories for fine-tuning, which endows HaluAgent with the capability for bilingual hallucination detection. Extensive experiments demonstrate that only using 2K samples for tuning LLMs, HaluAgent can perform hallucination detection on various types of tasks and datasets, achieving performance comparable to or even higher than GPT-4 without tool enhancements on both in-domain and out-of-domain datasets. We release our dataset and code at https://github.com/RUCAIBox/HaluAgent.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2406.11277
Document Type :
Working Paper