Summary of Can Large Language Models Do Analytical Reasoning?, by Yebowen Hu et al.
Can Large Language Models do Analytical Reasoning?
by Yebowen Hu, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Hassan Foroosh, Dong Yu, Fei Liu
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the application of Large Language Models (LLMs) in sports analytics, specifically focusing on counting points scored by teams in NBA and NFL games. The study uses various LLMs, including GPT-4, Claude-2.1, GPT-3.5, Gemini-Pro, and Llama-2-70b, to develop an analytical reasoning approach that breaks down play-by-play data into smaller segments and solves each segment individually before aggregating the results. The paper also investigates the effectiveness of different prompting techniques and the Chain of Thought (CoT) strategy, which improves outcomes for certain models but has negative effects on others. Surprisingly, most models struggle to accurately count total scores in NBA quarters despite performing well in NFL quarter scoring. The study concludes that task complexity depends on context length, information density, and related information presence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can help with sports statistics by using big language models. It tries different approaches to see which ones work best for counting points scored by teams in basketball and American football games. The researchers find that one approach, called divide-and-conquer, is really good at getting the right answers. They also test a way of thinking called Chain of Thought, which helps some models do better than others. But they’re surprised to see that most models struggle to count points correctly in basketball quarters, even though they do okay in football quarters. |
Keywords
» Artificial intelligence » Claude » Context length » Gemini » Gpt » Llama » Prompting
