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    <abstract>This paper proposes PiNose, which trains a probing model on offline self-consistency checking results, thereby circumventing the need for human-annotated data and achieving transferability across diverse data distributions. As the consistency check process is offline, PiNose reduces the computational burden of generating multiple responses by online consistency verification. Additionally, it examines various aspects of internal states prior to response decoding, contributing to more effective detection of factual inaccuracies. Experiment results on both factuality detection and question answering benchmarks show that PiNose achieves surpassing results than existing factuality detection methods.</abstract>
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%0 Conference Proceedings
%T Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking
%A Zhang, Xiaokang
%A Yao, Zijun
%A Zhang, Jing
%A Yun, Kaifeng
%A Yu, Jifan
%A Li, Juanzi
%A Tang, Jie
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhang-etal-2024-transferable
%X This paper proposes PiNose, which trains a probing model on offline self-consistency checking results, thereby circumventing the need for human-annotated data and achieving transferability across diverse data distributions. As the consistency check process is offline, PiNose reduces the computational burden of generating multiple responses by online consistency verification. Additionally, it examines various aspects of internal states prior to response decoding, contributing to more effective detection of factual inaccuracies. Experiment results on both factuality detection and question answering benchmarks show that PiNose achieves surpassing results than existing factuality detection methods.
%R 10.18653/v1/2024.acl-long.668
%U https://aclanthology.org/2024.acl-long.668
%U https://doi.org/10.18653/v1/2024.acl-long.668
%P 12348-12364

Markdown (Informal)

[Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking](https://aclanthology.org/2024.acl-long.668) (Zhang et al., ACL 2024)

• Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking (Zhang et al., ACL 2024)

ACL

• Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, and Jie Tang. 2024. Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12348–12364, Bangkok, Thailand. Association for Computational Linguistics.