Fine-grained Hallucination Detection and Editing for Language Models
CoRR(2024)
摘要
Large language models (LMs) are prone to generate diverse factually incorrect
statements, which are widely called hallucinations. Current approaches
predominantly focus on coarse-grained automatic hallucination detection or
editing, overlooking nuanced error levels. In this paper, we propose a novel
task – automatic fine-grained hallucination detection – and present a
comprehensive taxonomy encompassing six hierarchically defined types of
hallucination. To facilitate evaluation, we introduce a new benchmark that
includes fine-grained human judgments on two LM outputs across various domains.
Our analysis reveals that ChatGPT and Llama 2-Chat exhibit hallucinations in
60
hallucinations fall into categories that have been underexplored. As an initial
step to address this, we train FAVA, a retrieval-augmented LM by carefully
designing synthetic data generations to detect and correct fine-grained
hallucinations. On our benchmark, our automatic and human evaluations show that
FAVA significantly outperforms ChatGPT on fine-grained hallucination detection
by a large margin though a large room for future improvement still exists.
FAVA's suggested edits also improve the factuality of LM-generated text,
resulting in 5-10
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