Unverified50% confidenceFactExact time
The paper's core conclusion is that multiple-choice formats systematically overestimate models' actual generation capabilities.
1
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50%
Confidence
Long-term
Relevance
9/24/2026
First Seen
Sources
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Related Claims
UnverifiedThis research conclusion refutes the popular claim that models adaptively adjust their computation based on problem difficulty.77% similarUnverifiedPrevious research has mostly attributed over-refusal to static representation overlap, where benign prompts and harmful prompts are too close to each other in the model's internal representation space.74% similarVerifiedSome models carry the risk of over-optimization toward test sets, causing leaderboard scores to deviate from real-world performance (benchmark saturation).74% similarVerifiedExcessive safety fine-tuning can cause models to over-refuse, undermining their practical utility.73% similarUnverifiedThe overconfidence of a model answering directly while skipping retrieval is technically referred to as miscalibration, meaning there is a systematic deviation between confidence and actual accuracy.73% similar
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