Summary of Are Llms Capable Of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data, by Xiao Liu et al.
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data
by Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng
First submitted to arxiv on: 27 Feb 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 proposed QRData benchmark aims to evaluate Large Language Models’ ability in statistical and causal reasoning with real-world data. The benchmark consists of 411 questions accompanied by data sheets from textbooks, online learning materials, and academic papers. Additionally, an auxiliary set of 290 text-only questions (QRText) is introduced to compare models’ quantitative reasoning abilities on data and text. Various natural language reasoning, program-based reasoning, and agent reasoning methods are evaluated on diverse models, including Chain-of-Thought, Program-of-Thoughts, ReAct, and code interpreter assistants. The strongest model GPT-4 achieves an accuracy of 58%, while open-source models like Deepseek-coder-instruct, a code LLM pretrained on 2T tokens, get the highest accuracy of 37%. Analysis reveals that models struggle in data analysis and causal reasoning, particularly when using causal knowledge and provided data simultaneously. The study highlights the limitations of current language models in quantitative reasoning and provides insights for future improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are trying to improve their ability to understand and work with real-world data. To test how well they’re doing, researchers created a special set of questions called QRData. This benchmark has 411 questions that come with data sheets from textbooks, online learning materials, and academic papers. They also added another set of 290 questions that are just text (QRText). The goal is to see which models can best answer these questions and how well they do when working with data or just reading text. Some models did better than others, but overall, there’s still a lot of room for improvement. The study shows that language models struggle when trying to analyze data and figure out cause-and-effect relationships. |
Keywords
» Artificial intelligence » Gpt » Online learning
