Summary of Functional Benchmarks For Robust Evaluation Of Reasoning Performance, and the Reasoning Gap, by Saurabh Srivastava et al.
Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap
by Saurabh Srivastava, Annarose M B, Anto P V, Shashank Menon, Ajay Sukumar, Adwaith Samod T, Alan Philipose, Stevin Prince, Sooraj Thomas
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 AI research paper proposes a framework for evaluating the reasoning capabilities of language models, using functional variants of benchmarks. The goal is to assess whether models can adapt to changing problem scenarios, as opposed to simply memorizing static problems. To test this, the authors created a functional variant of the MATH benchmark, dubbed MATH(), and found significant “reasoning gaps” between state-of-the-art models’ performance on static and functional versions. The study highlights the importance of considering model flexibility in evaluating their reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how well language models can solve problems that change slightly from one moment to another. Currently, most models are good at solving fixed problems, but they struggle when the problem changes. To test this, the authors created a special version of a math test called MATH(). They found that even top-performing models struggled with these changing problems, with some models performing much worse than others. The study suggests that building models that can adapt well to changing situations could be important for making progress in this field. |
