Summary of Lost in the Logic: An Evaluation Of Large Language Models’ Reasoning Capabilities on Lsat Logic Games, by Saumya Malik
Lost in the Logic: An Evaluation of Large Language Models’ Reasoning Capabilities on LSAT Logic Games
by Saumya Malik
First submitted to arxiv on: 23 Sep 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 thesis evaluates the performance of Large Language Models (LLMs) on the Logic Games section of the Law School Admissions Test (LSAT), which is a complex logical reasoning task. The study constructs a dataset of LSAT logic games and their metadata, and extensively evaluates LLMs’ performance in a Chain-of-Thought prompting setting. Although initial results are weak, adapting ideas from Reflexion improves accuracy to 70% for GPT-4 and 46% for GPT-3.5 on a smaller subset of the dataset. The study analyzes the types of logic games that models perform better or worse on, providing insights on LLMs’ logical reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how good Large Language Models (LLMs) are at solving tricky logic puzzles from the Law School Admissions Test. It’s like a big test to see if they can figure out hard problems! The researchers made a special set of questions and tested the LLMs’ answers. They found that some LLMs were really good, but others needed help. The study shows what kinds of puzzles are easy or hard for LLMs to solve. |
Keywords
» Artificial intelligence » Gpt » Prompting
