FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li
Abstract
Detecting factual errors of textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions. LLMs’ inability to attribute their claims to external knowledge and their tendency to hallucinate makes it difficult to rely on their responses. Humans, too, are prone to factual errors in their writing. Since manual detection and correction of factual er- rors is labor-intensive, developing an automatic approach can greatly reduce human effort. We present a prototype tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors using the collected evidence. Initial empirical evaluation on fact error detection (77-85% F1) shows the potential of our tool.- Anthology ID:
- 2023.emnlp-demo.10
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yansong Feng, Els Lefever
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 124–130
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-demo.10
- DOI:
- 10.18653/v1/2023.emnlp-demo.10
- Cite (ACL):
- Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, and Yunyao Li. 2023. FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 124–130, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (Fatahi Bayat et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.emnlp-demo.10.pdf