Large language models (LMs) are prone to generate factual errors, which are often called hallucinations. In this paper, we introduce a comprehensive taxonomy of hallucinations and argue that hallucinations manifest in diverse forms, each requiring varying degrees of careful assessments to verify factuality. We propose a novel task of automatic fine-grained hallucination detection and construct a new evaluation benchmark, FavaBench, that includes about one thousand fine-grained human judgments on three LM outputs across various domains. Our analysis reveals that ChatGPT and Llama2-Chat (70B, 7B) exhibit diverse types of hallucinations in the majority of their outputs in information-seeking scenarios. We train FAVA, a retrieval-augmented LM by carefully creating synthetic data to detect and correct fine-grained hallucinations. On our benchmark, our automatic and human evaluations show that FAVA significantly outperforms ChatGPT and GPT-4 on fine-grained hallucination detection, and edits suggested by FAVA improve the factuality of LM-generated text.
@article{arxiv.2401.06855,
title = {Fine-grained Hallucination Detection and Editing for Language Models},
author = {Abhika Mishra and Akari Asai and Vidhisha Balachandran and Yizhong Wang and Graham Neubig and Yulia Tsvetkov and Hannaneh Hajishirzi},
journal= {arXiv preprint arXiv:2401.06855},
year = {2024}
}
Comments
Our code, data, and demo are available at https://fine-grained-hallucination.github.io. Published as a conference paper at COLM 2024