3 citations · 3 across the 4 of their papers we have counts for
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cs.CL2026
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…
cs.CL2025★ 3 cited
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…
cs.CL2025
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…