A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection

Shiping Yang, Renliang Sun, Xiaojun Wan

Abstract
Large Language Models (LLMs) have shown their ability to collaborate effectively with humans in real-world scenarios. However, LLMs are apt to generate hallucinations, i.e., makeup incorrect text and unverified information, which can cause significant damage when deployed for mission-critical tasks. In this paper, we propose a self-check approach based on reverse validation to detect factual errors automatically in a zero-resource fashion. To facilitate future studies and assess different methods, we construct a hallucination detection benchmark named PHD, which is generated by ChatGPT and annotated by human annotators. Contrasting previous studies of zero-resource hallucination detection, our method and benchmark concentrate on passage-level detection instead of sentence-level. We empirically evaluate our method and existing zero-resource detection methods on two datasets. The experimental results demonstrate that the proposed method considerably outperforms the baselines while costing fewer tokens and less time. Furthermore, we manually analyze some hallucination cases that LLM failed to capture, revealing the shared limitation of zero-resource methods.
Anthology ID:
2023.findings-emnlp.256
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3898–3908
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.256
DOI:
10.18653/v1/2023.findings-emnlp.256
Bibkey:
Cite (ACL):
Shiping Yang, Renliang Sun, and Xiaojun Wan. 2023. A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3898–3908, Singapore. Association for Computational Linguistics.
Cite (Informal):
A New Benchmark and Reverse Validation Method for Passage-level Hallucination Detection (Yang et al., Findings 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2023.findings-emnlp.256.pdf
PDF Search