Summary of Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights From the Neubaroco Dataset, by Kentaro Ozeki et al.
Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset
by Kentaro Ozeki, Risako Ando, Takanobu Morishita, Hirohiko Abe, Koji Mineshima, Mitsuhiro Okada
First submitted to arxiv on: 8 Aug 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 This paper investigates whether current large language models (LLMs) can accurately perform logical reasoning in natural language, particularly with regards to syllogistic reasoning. The study presents a dataset called NeuBAROCO, comprising syllogistic reasoning problems in English and Japanese. Experiments with leading LLMs show that these models exhibit reasoning biases similar to humans, along with other error tendencies. The results highlight the need for improvement in reasoning problems where the relationship between premises and hypotheses is neither entailment nor contradiction. A new Chain-of-Thought prompting method was used to analyze the reasoning process of LLMs, revealing that primary limitations lie in the reasoning process itself rather than the interpretation of syllogisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper asks if big computer programs (called large language models) can do logical thinking as well as humans. The researchers created a special set of problems called NeuBAROCO to test this. They looked at how these computer programs did on this task and found that they make mistakes, just like humans. But the main problem is not that the computers don’t understand the rules, it’s that they have trouble making good decisions. |
Keywords
» Artificial intelligence » Prompting
