The future of UI is generative. "Just in time" design that customizes to what you're doing and when/where you're doing it. And it'll keep adapting along with you – people aren't static either
@mustafasuleyman
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AI Model Evaluation Beyond Basic Accuracy Benchmarking Study
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Great work from @williamjurayj
, @jeff_cheng_77
, and @ben_vandurme to push us past just the basic accuracy benchmarking. So next time you get an "I don't know" answer from AI – just remember that could be a good thing. Full study here: https://
arxiv.org/pdf/2502.13962 -
Confidence Thresholds and Computational Resource Trade-offs in AI
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– Yes, higher confidence thresholds meant more questions went unanswered – something we either need to accept, depending on the risk level, or be willing to throw more compute in when confidence is critical
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Model Confidence Critical for High-Stakes AI Applications
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– Higher compute and confidence were extra important for model performance when wrong answers had serious consequences (like when they were weighted 20x). You wouldn't want a 51%-sure diagnosis of a terminal illness.
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Compute Power Increases Model Confidence and Overconfidence Risks
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– And good news! More compute = more confidence as well. (However, more compute made one model become more confident in wrong answers too. Something to keep an eye to make sure model confidence doesn’t tip into overconfidence.)
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Compute Power Drives AI Model Accuracy and Reliability
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– Yes, across the board more compute = more accuracy (although without any confidence thresholds, you'll still get wild guesses or wrong answers)
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Teaching Models Confidence and Uncertainty Recognition
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We know more compute results in higher accuracy, but are the models more confident those answers ARE accurate too? And how do we teach them when to say “I don’t know”? That’s what the research team wanted to find out.
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Compute Budget and Confidence Thresholds Impact Model Math Performance
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In the study, they measured how different combinations of compute budget and confidence thresholds (being at least 50% sure of the answer, etc.) affected models’ performance on a benchmark math test.
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Risk Penalty Weights Impact on Model Performance
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They also experimented with different risk levels. On one end of the spectrum: no penalty for wrong answers. On the other: the penalty of wrong answers weighted 20x more than the reward for correct ones. What they found:
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Johns Hopkins: LLMs Must Know When to Abstain
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You can't just be right, you have to know you're right. Good advice for LLMs, according to new Johns Hopkins research. Sometimes no answer is better than a wrong one – life or death choices in medicine, for example, or big financial decisions.