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Seq@K: Evaluating Language Models Under Sequential Feedback

Source
arxiv.org
Author
Thomson Yen, Gilbert Yang, Naimeng Ye, Minghui Chen, Dong Xia, Tianyi Shen, Yechan Jeon, Yaqin Chen, Daksh Mittal, Fangyu Wu, Hongseok Namkoong
Date
Why it matters

Pass@1 misses how well a model uses feedback after failures, which is how agents work. Seq@K shows model rankings can change under sequential feedback, so choice may alter which model you pick.

Terms in this piece · Glossary
  • benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
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