This work proposes a two stage detection framework that first identifies suspicious conversations using a tailored ensemble classification model and compares its modular approach against a Dual LLM baseline that performs detection and judgment using different language models.
Ali Şenol, Garima Agrawal, Huanmin Liu· arXiv.org· 4 citations
Findings show that self-assessment can support explicit, tunable answer-or-abstain decisions when an unsupported commitment is more costly than referral or review.
This work introduces the Epistemic Honesty Quotient (EHQ), which reports three observable sub-scores across two operational axes (epistemic restraint and substantive-answer calibration), and constructs EHQ-3000, a 3,000-question benchmark spanning Fabricated Entity, Post-Cutoff Event, Hyper-Niche True, and Context-Cond...
RQEval is proposed, a unified multi-dimensional framework for measuring LLM reasoning quality from a behavioral perspective that operationalizes six theoretically grounded dimensions rooted in cognitive science and introduces deployment-aware aggregation, enabling context-specific model selection beyond accuracy-based...
Ali Şenol, Garima Agrawal, Huan Liu· Big Data and Cognitive Compu...· 2 citations
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