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 answer commitment condit...
arXiv:2604. 03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing over expressing uncertainty.
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.
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.
arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.
arXiv:2608.21721v1 Announce Type: new Abstract: Correcting health misinformation in dialogue requires more than producing a factual rebuttal: users differ in what they know, what they believe, and wh...
arXiv:2602. 23971v4 Announce Type: replace-cross Abstract: Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-stakes advisory and social contexts.
arXiv:2609.24799v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment of large language models, and PTQ methods are usually optimized and evaluated with generi...
arXiv:2607.11414v2 Announce Type: replace Abstract: Large language models (LLMs) in financial applications fail most consequentially when they are confidently wrong. Hedged, uncertain answers invite...
arXiv:2607.14109v2 Announce Type: replace Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central...
arXiv:2610.35694v1 Announce Type: new Abstract: Conversational QA systems should fix errors when users push back and signal which answers to trust. We split each follow-up into its speech act (re-ask...
The paper introduces PsyJev, a System‑1 model for psychological analysis that directly outputs distributions over answers conditioned on questions. It leverages the PsyJudgeBench benchmark, which contains 13 annotated datasets and 82 questions across six psychological domains, and employs a joint supervised training followed by Judgment‑Anchored RLCD to adapt logit‑space reward optimization. Experiments on Qwen3.5‑based models show that the 9B variant achieves an average test F1 of 67.56, outperforming the strongest external baseline by 2.54 points.