Long-form Hallucination Detection with Self-elicitation
Keywords: hallucination, knowledge graph, large language models, medical QA
TL;DR: A framework called SelfElicit that synergizes the self-elicitation of inherent knowledge of LLMs and long-form contextual understanding with a knowledge hypergraph for hallucination detection.
Abstract: While Large Language Models (LLMs) have exhibited impressive performance in long-form question-answering tasks, they frequently present a hazard of producing factual inaccuracies or hallucinations. An effective strategy to mitigate this hazard is to leverage off-the-shelf LLMs to detect hallucinations after the generation. The primary challenge resides in the comprehensive elicitation of the intrinsic knowledge acquired during their pre-training phase. However, existing methods that employ complex reasoning chains predominantly fall short of addressing this issue. Moreover, since existing methods for hallucination detection tend to decompose the text into isolated statements, they are unable to understand the inherent in-context semantics in long-form content. In this paper, we propose a novel framework, SelfElicit, which synergizes the self-elicitation of intrinsic knowledge of large language models and long-form continuity understanding. Specifically, we leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge, which is integrated with graph structures to alleviate induced hallucinations and guide the factual evaluation by effectively organizing the elicited knowledge. Extensive experiments on real-world QA datasets demonstrate the effectiveness of self-elicitation and the superiority of our proposed method.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 1926
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