Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
Probing the difficulty perception mechanism of large language models
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DARE co-evolves difficulty estimation and policy in RL for LLMs to improve training efficiency, final performance, and inference speed by using tailored strategies for different difficulty levels.
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
citing papers explorer
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Cognitive offloading and the speedup illusion in human-AI interaction
Preregistered behavioral study identifies a speedup illusion where users overestimate time savings from AI assistance on cognitive tasks despite no actual difference in completion times.
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DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
DARE co-evolves difficulty estimation and policy in RL for LLMs to improve training efficiency, final performance, and inference speed by using tailored strategies for different difficulty levels.
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RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment
RouteLMT learns to route MT requests to large or small LLMs by predicting marginal quality gain from small-model token representations, yielding a better quality-budget Pareto frontier than baselines.
- Reasoning Models Don't Just Think Longer, They Move Differently