Summary of Learning Beyond Pattern Matching? Assaying Mathematical Understanding in Llms, by Siyuan Guo et al.
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs
by Siyuan Guo, Aniket Didolkar, Nan Rosemary Ke, Anirudh Goyal, Ferenc Huszár, Bernhard Schölkopf
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 A study explores the capabilities of pre-trained language models (LLMs) as general scientific assistants by assessing their understanding of mathematical skills required to solve problems. The research focuses on how LLMs learn from information during in-context learning or instruction-tuning, leveraging complex knowledge structures within mathematics. Inspired by the Neural Tangent Kernel (NTK), the authors propose NTK-Eval, a method to analyze changes in LLM’s probability distribution after training on different math data. The analysis reveals evidence of domain understanding during in-context learning but mixed results for instruction-tuning, suggesting varying levels of understanding across different mathematical skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are helping scientists make new discoveries! Researchers want to know how well these models understand math problems. They tested the models’ ability to learn from examples and instructions about math concepts. The results show that the models can learn from examples, but not always from instructions. This matters because it could help us create better tools for scientists. |
Keywords
» Artificial intelligence » Instruction tuning » Probability
