Detecting factual errors in 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 errors is labor-intensive, developing an automatic approach can greatly reduce human effort. We present FLEEK, 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 FLEEK. A video demo of FLEEK can be found at https://youtu.be/NapJFUlkPdQ.
@article{arxiv.2310.17119,
title = {FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge},
author = {Farima Fatahi Bayat and Kun Qian and Benjamin Han and Yisi Sang and Anton Belyi and Samira Khorshidi and Fei Wu and Ihab F. Ilyas and Yunyao Li},
journal= {arXiv preprint arXiv:2310.17119},
year = {2023}
}