3 citations · 3 across the 4 of their papers we have counts for
5 papers
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
Ali Şenol
Large language models can produce fluent answers when their factual support is weak. This paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes answ…
Do Large Language Models Know What They Don't Know II? A Fully Behavioral, Non-Cognitive Measure of Epistemic Honesty
Ali Şenol, H. Russell Bernard, Huan Liu
Large Language Models (LLMs) are frequently confident, eloquent, and well versed. A natural question arises: do they know what they don't know? To answer this question, we borrow t…
Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework
Ali Şenol, Garima Agrawal, Huan Liu
Despite remarkable progress on reasoning benchmarks, current LLM evaluation practice remains anchored to final-answer correctness, providing limited insight into how models reason,…
Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
Ali Şenol, Garima Agrawal, Huan Liu
Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)-i.e., semantic or topical shifts that alte…
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment
Ali Senol, Ali Şenol, Garima Agrawal +1
Detecting fake interactions in digital communication platforms remains a challenging and insufficiently addressed problem. These interactions may appear as harmless spam or escalat…