FLEEK: Factual Error Detection and Correction with Evidence Retrieved
  from External Knowledge
Farima Fatahi Bayat Kun Qian Benjamin Han Yisi Sang Anton Belyi Samira Khorshidi Fei Wu Ihab F. Ilyas Yunyao Li
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.