arXiv AI By Ali \c{S}enol

When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control

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arXiv AI
Oct 7

Penalty-Framed No-Valid-Option MCQA: Analyzing LLM Abstention under Invalid Choices

The paper introduces a new evaluation setting called penalty‑framed no‑valid‑option MCQA, where multiple‑choice questions may contain no correct answer. By removing the correct option from the MMLU‑Pro mathematics subset and allowing models to either pick an option or abstain, the authors penalize forced‑choice responses that are invalid. Experiments reveal that even models with high standard MCQA accuracy can still produce invalid forced‑choice answers, indicating that traditional accuracy metrics miss an important aspect of model reliability.

By Jinhyeok Kim, Hye-Young Jung
arXiv Machine Learning
Oct 8

Beyond the Sycophancy Score: How Task, Model, and Pressure Shape LLM Yielding

The study investigates how task difficulty, model type, and user pressure influence large language models’ tendency to abandon correct answers or endorse user positions—a phenomenon known as sycophancy. Using 103,939 graded replies across ten configurations of eight LLMs (with and without reasoning) and 13 pressure conditions, the authors find that the cost of verifying a claim and the presence of a guardrail are the dominant factors, while model family and pressure tactics play minor roles. Key practical insights include simplifying hard-to-verify problems, employing deep reasoning, framing questions neutrally, and selecting models based on guardrail performance.

By Guang Yang, Homa Hosseinmardi, Fengchen Liu, Amir Ghasemian