Summary of Easy2hard-bench: Standardized Difficulty Labels For Profiling Llm Performance and Generalization, by Mucong Ding et al.
Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization
by Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Easy2Hard-Bench, a novel dataset collection that addresses the lack of fine-grained difficulty annotations for profile language models (LLMs). The dataset comprises six benchmark datasets spanning various domains, including mathematics, programming problems, chess puzzles, and reasoning questions. Each problem is annotated with numerical difficulty scores based on human performance data or LLMs’ leaderboard attempts. By leveraging established ranking systems like Item Response Theory (IRT) and Glicko-2 models, the authors uniformly assign difficulty scores to problems. The dataset’s uniqueness lies in its higher proportion of challenging problems compared to previous collections. The paper also provides a comprehensive analysis of six state-of-the-art LLMs’ performance and generalization capabilities across varying levels of difficulty, aiming to inspire future research in LLM generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new dataset called Easy2Hard-Bench that helps language models learn from different types of problems. The dataset has six categories of problems, like math and programming puzzles, chess, and logical reasoning questions. Each problem is labeled with how hard it is to solve, based on how well humans or language models do on it. This helps researchers study how language models can generalize their learning to harder or easier problems. The paper also shows how well six top language models perform on these problems, which could help improve future research. |
Keywords
» Artificial intelligence » Generalization
