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cs.CL2025
Anticipatory Evaluation of Language Models: Predicting LLM Benchmark Scores from Descriptions
Jungsoo Park, Ethan Mendes, Gabriel Stanovsky +1
Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how much can be inferred about eval…
cs.LG2025
Language Models can Self-Improve at State-Value Estimation for Better Search
Ethan Mendes, Alan Ritter
Collecting ground-truth rewards or human demonstrations for multi-step reasoning tasks is often prohibitively expensive, particularly in interactive domains such as web tasks. We i…