Summary of Are Large Language Models Good Statisticians?, by Yizhang Zhu et al.
Are Large Language Models Good Statisticians?
by Yizhang Zhu, Shiyin Du, Boyan Li, Yuyu Luo, Nan Tang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 StatQA, a benchmark designed to evaluate the proficiency of Large Language Models (LLMs) in statistical analysis tasks. It comprises 11,623 examples tailored to assess LLMs’ capabilities in hypothesis testing methods. The authors experiment with representative LLMs using various prompting strategies and find that even state-of-the-art models like GPT-4o achieve only a best performance of 64.83%, indicating room for improvement. Fine-tuned LLMs exhibit marked improvements, outperforming in-context learning-based methods. Comparative human experiments reveal striking contrasts between LLM errors (primarily applicability) and human errors (mainly statistical task confusion). This divergence highlights distinct areas of proficiency and deficiency, suggesting the potential for combining LLM and human expertise to achieve complementary strengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new benchmark called StatQA that tests Large Language Models’ ability to do math and science problems. It’s like a big quiz with 11,623 questions. The researchers used different models and ways of asking the questions to see how well they did. They found that even the best model only got about 65% correct, which means there’s still room for improvement. Some models were better than others, especially those that were trained on specific tasks. When humans took the quiz, they made different mistakes than the computers, so this could be a way to make computers and humans work together better. |
Keywords
» Artificial intelligence » Gpt » Prompting
