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    <abstract>Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated annotation process, especially across numerous vertical domains and in the face of rapid LLM advancements. In this study, we introduce an approach that automatically generates both faithful and hallucinated outputs by rewriting system responses. Experimental findings demonstrate that a T5-base model, fine-tuned on our generated dataset, surpasses state-of-the-art zero-shot detectors and existing synthetic generation methods in both accuracy and latency, indicating efficacy of our approach.</abstract>
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%0 Conference Proceedings
%T Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses
%A Zhang, Dongxu
%A Gangal, Varun
%A Lattimer, Barrett
%A Yang, Yi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F zhang-etal-2024-enhancing-hallucination
%X Detecting hallucinations in large language model (LLM) outputs is pivotal, yet traditional fine-tuning for this classification task is impeded by the expensive and quickly outdated annotation process, especially across numerous vertical domains and in the face of rapid LLM advancements. In this study, we introduce an approach that automatically generates both faithful and hallucinated outputs by rewriting system responses. Experimental findings demonstrate that a T5-base model, fine-tuned on our generated dataset, surpasses state-of-the-art zero-shot detectors and existing synthetic generation methods in both accuracy and latency, indicating efficacy of our approach.
%U https://aclanthology.org/2024.findings-acl.789
%P 13321-13332

Markdown (Informal)

[Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses](https://aclanthology.org/2024.findings-acl.789) (Zhang et al., Findings 2024)

• Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses (Zhang et al., Findings 2024)

ACL

• Dongxu Zhang, Varun Gangal, Barrett Lattimer, and Yi Yang. 2024. Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses. In Findings of the Association for Computational Linguistics ACL 2024, pages 13321–13332, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.