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cs.CL2026
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
Tony Lee, Percy Liang
Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools? We study this setting start…
cs.CL2025
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi, Hejie Cui, Miguel Fuentes +78
While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinica…
cs.CL2025
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making
Manling Li, Shiyu Zhao, Qineng Wang +12
We aim to evaluate Large Language Models (LLMs) for embodied decision making. While a significant body of work has been leveraging LLMs for decision making in embodied environments…