arXiv:2607.08456v1 Announce Type: new 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, which cannot tell these apart. Across five instruction-tuned models from three families (2B to 14B), we find they are separate axes. Ordinary answer-confidence tracks whether an answer is right but is nearly blind to whether the question is answerable; a linear probe on hidden states does the reverse. The blind spot do
The continuous development and deployment of LLMs necessitate deeper understanding and improvement of their core functionalities, as evidenced by ongoing research into their abstention capabilities.
Improving LLM abstention on incorrect answers and unanswerable questions is crucial for their reliability in high-stakes applications and for building trust in AI systems.
The research suggests a new approach to LLM abstention beyond single confidence scores, allowing for more nuanced and accurate refusal of information.
- · AI developers
- · Enterprises deploying LLMs
- · Users of AI systems
- · AI safety researchers
- · Developers of unreliable LLM applications
More reliable and trustworthy AI models in various applications.
Accelerated adoption of LLMs in critical sectors like healthcare, finance, and defense.
Reduced risk of AI-induced misinformation or erroneous decision-making, potentially influencing regulatory frameworks.
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