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Detecting factual errors in textual information, whether generated by largelanguage models (LLM) or curated by humans, is crucial for making informeddecisions. LLMs' inability to attribute their claims to external knowledge andtheir tendency to hallucinate makes it difficult to rely on their responses.Humans, too, are prone to factual errors in their writing. Since manualdetection and correction of factual errors is labor-intensive, developing anautomatic approach can greatly reduce human effort. We present FLEEK, aprototype tool that automatically extracts factual claims from text, gathersevidence from external knowledge sources, evaluates the factuality of eachclaim, and suggests revisions for identified errors using the collectedevidence. Initial empirical evaluation on fact error detection (77-85\% F1)shows the potential of FLEEK. A video demo of FLEEK can be found athttps://youtu.be/NapJFUlkPdQ.

FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge