VeriTrail: Detecting hallucination and tracing provenance in multi-step AI workflows
Many applications of language models (LMs) involve generating content based on source material, such as answering questions, summarizing information, and drafting documents. A critical challenge for these applications is that LMs may produce content that is not supported by the source text – a phenomenon known as “closed-domain hallucination.”1 Existing methods for detecting closed-domain hallucination typically compare a given LM output
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