Summary of Mmlu-pro+: Evaluating Higher-order Reasoning and Shortcut Learning in Llms, by Saeid Asgari Taghanaki et al.
MMLU-Pro+: Evaluating Higher-Order Reasoning and Shortcut Learning in LLMs
by Saeid Asgari Taghanaki, Aliasgahr Khani, Amir Khasahmadi
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The paper introduces MMLU-Pro+, a challenging benchmark for large language models (LLMs) that assesses their ability to engage in complex reasoning and resist simplistic problem-solving strategies. The benchmark builds upon MMLU-Pro, incorporating questions with multiple correct answers across diverse domains. It tests the LLMs’ higher-order reasoning abilities and susceptibility to anchoring bias. The results show significant performance gaps between six state-of-the-art LLMs, revealing variations in their reasoning abilities and bias susceptibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark for language models that is harder than previous ones because it makes them think more deeply about the answers. It asks questions with multiple correct answers from different areas of knowledge, like science or history. This helps figure out if the models are just doing quick workarounds or actually understanding what they’re saying. The results show that even top-performing language models do worse on this new test. |
