JointCQ: Improving Factual Hallucination Detection with Joint Claim and Query Generation
This addresses hallucination issues in LLMs for improving trustworthiness, but it is incremental as it focuses on enhancing specific pipeline stages.
The paper tackled the problem of factual hallucination detection in large language models by introducing JointCQ, a joint claim-and-query generation framework, which outperformed previous methods on multiple open-domain QA benchmarks.
Current large language models (LLMs) often suffer from hallucination issues, i,e, generating content that appears factual but is actually unreliable. A typical hallucination detection pipeline involves response decomposition (i.e., claim extraction), query generation, evidence collection (i.e., search or retrieval), and claim verification. However, existing methods exhibit limitations in the first two stages, such as context loss during claim extraction and low specificity in query generation, resulting in degraded performance across the hallucination detection pipeline. In this work, we introduce JointCQ https://github.com/pku0xff/JointCQ, a joint claim-and-query generation framework designed to construct an effective and efficient claim-query generator. Our framework leverages elaborately designed evaluation criteria to filter synthesized training data, and finetunes a language model for joint claim extraction and query generation, providing reliable and informative inputs for downstream search and verification. Experimental results demonstrate that our method outperforms previous methods on multiple open-domain QA hallucination detection benchmarks, advancing the goal of more trustworthy and transparent language model systems.