Summary of Do Large Language Models Understand Logic or Just Mimick Context?, by Junbing Yan et al.
Do Large Language Models Understand Logic or Just Mimick Context?
by Junbing Yan, Chengyu Wang, Jun Huang, Wei Zhang
First submitted to arxiv on: 19 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 Large language models (LLMs) have made significant progress in complex tasks like logical reasoning and symbolic inference, thanks in part to their ability to learn from context and use few-shot prompting. However, researchers have not fully understood the reasons behind this success. Do LLMs truly understand logical rules or are they simply making educated guesses based on probabilistic mappings learned through context? This study investigated the reasoning capabilities of LLMs on two datasets using counterfactual methods to modify contextual text and logical concepts. The results show that while LLMs may produce correct answers, they do not actually comprehend logical rules. Instead, their in-context learning simply increases the likelihood of producing accurate responses. Surprisingly, altering specific words or concepts in the context can significantly disrupt LLM outputs, leading to counterintuitive responses. This study provides crucial insights into the limitations of LLMs, highlighting the need for more robust mechanisms to ensure reliable logical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and respond to human-like language. They’re really good at doing things like solving puzzles and making connections between ideas. But have you ever wondered how they do it? Do they actually understand what’s going on, or are they just guessing? This study looked into this question by testing how well these models can reason about logical rules. The results showed that while the models are really good at getting the right answers, they don’t actually understand the underlying rules. Instead, they’re using something called “in-context learning” to make educated guesses. But here’s the surprising part: if you change a few words or concepts in the context, the model’s responses can completely fall apart! This study helps us understand what these models are capable of and what they need to improve. |
Keywords
» Artificial intelligence » Few shot » Inference » Likelihood » Prompting
