Snowflake/dare-bench
DARE-Bench [ICLR 2026] DARE-Bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science Fan Shu1, Yite Wang2, Ruofan Wu1, Boyi Liu2, Zhewei Yao2, Yuxiong He2, Feng Yan1 1University of Houston 2Snowflake AI Research 🔎 Overview DARE-Bench (ICLR 2026) is a benchmark for evaluating LLM agents on data science tasks, focusing on modeling and instruction fidelity. This Hugging Face repository provides a selected subset of the full benchmark for public… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/dare-bench.
| Author / organization | Snowflake |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 617 (as reported by Hugging Face) |
| Likes | 6 |
| Created | 25 February 2026 |
| Last updated at source | 2 March 2026 |
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- 16 Oct 2026 Description: DARE-Bench [ICLR 2026] DARE-Bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science Fan Shu1, Yite Wang2, Ruofan Wu1, Boyi Liu2, Zhewei Yao2, Yuxiong He2, Feng Yan1 1University of Houston 2Snowflake AI Research 🔎 Overvi → DARE-Bench [ICLR 2026] DARE-Bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science Fan Shu1, Yite Wang2, Ruofan Wu1, Boyi Liu2, Zhewei Yao2, Yuxiong He2, Feng Yan1 1University of Houston 2Snowflake AI Research 🔎 Overvi
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