PulseAugur
EN
LIVE 09:59:25

LLMs struggle to distinguish between wrong answers and unanswerable questions

By PulseAugur Editorial · [3 sources] ·

Researchers have identified two distinct axes of abstention in large language models: answer correctness and question answerability. Current models struggle to differentiate between refusing to answer an incorrect question and refusing to answer a question that should not be answered at all, such as those with false premises. A new approach using a hidden-state probe, rather than simple confidence scores, can better identify unanswerable questions, leading to improved precision in model responses. AI

IMPACT This research could lead to LLMs that are more reliable in refusing to answer inappropriate or incorrect questions, improving user trust and safety.

RANK_REASON The cluster contains two academic papers published on arXiv discussing LLM behavior and evaluation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs struggle to distinguish between wrong answers and unanswerable questions

How we ranked this

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
The cluster contains two academic papers published on arXiv discussing LLM behavior and evaluation.
Source corroboration
3 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
62 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Benedikt J. Wagner ·

    Two Axes of LLM Abstention: Answer Correctness and Question Answerability

    arXiv:2607.08456v1 Announce Type: cross Abstract: A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones resting on a false premise. The usual recipe thresholds a single confidence score…

  2. arXiv cs.AI TIER_1 English(EN) · Robin Staab, Jasper Dekoninck, Maximilian Baader, Martin Vechev ·

    Adaptive Generation of Bias-Eliciting Questions for LLMs

    arXiv:2510.12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide. Despite their widespread adoption, growing reliance on their outputs raises significant co…

  3. arXiv cs.AI TIER_1 English(EN) · Benedikt J. Wagner ·

    Two Axes of LLM Abstention: Answer Correctness and Question Answerability

    A model should refuse two different things: answers it would get wrong, and questions it should not answer at all, such as unanswerable ones or ones resting on a false premise. The usual recipe thresholds a single confidence score, which cannot tell these apart. Across five instr…

RELATED ENTITIES

RELATED TOPICS