Papers by Emmanuel Candes
Can Unconfident LLM Annotations Be Used for Confident Conclusions? (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have shown high agreement with human raters across a variety of tasks, demonstrating potential to ease the challenges of human data collection. |
| Approach: | They propose a method that combines LLM annotations and LLM confidence indicators to strategically select which human annotations to use. |
| Outcome: | The proposed method produces accurate estimates and valid confidence intervals while reducing the number of human annotations by over 25%. |
s1: Simple test-time scaling (2025.emnlp-main)
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Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candes, Tatsunori Hashimoto
| Challenge: | OpenAI’s o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. |
| Approach: | They curate a small dataset s1K with 1,000 reasoning questions based on three criteria we validate through ablations: difficulty, diversity, and quality. |
| Outcome: | The proposed model exceeds o1-preview on competition math questions by up to 27% (MATH and AIME24). |