Summary of Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models Via Generic Fact Guidance, by Kai Xiong et al.
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
by Kai Xiong, Xiao Ding, Ting Liu, Bing Qin, Dongliang Xu, Qing Yang, Hongtao Liu, Yixin Cao
First submitted to arxiv on: 14 Mar 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 Medium Difficulty summary: Large language models (LLMs) have achieved impressive performance and explainability across various reasoning scenarios, demonstrating significant progress towards human-like intelligence. However, when faced with simple questions supported by generic facts, LLMs struggle to abstract and apply these facts consistently and precisely, revealing a deficiency in abstract reasoning abilities. This has sparked debate about whether LLMs genuinely reason or merely memorize. To investigate this, we designed a preliminary study to quantify and explore the abstract reasoning abilities of existing LLMs. Our findings show a substantial discrepancy between general and abstract reasoning performances. To address this issue, we developed an abstract reasoning dataset (AbsR) and a meaningful learning paradigm to teach LLMs how to leverage generic facts for reasoning purposes. The results demonstrate that our approach not only improves LLM’s general reasoning performance but also enhances their capacity for abstract reasoning, moving beyond simple memorization or imitation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Large language models have gotten very good at answering questions and explaining themselves, which is cool! However, they struggle to use general facts to answer new questions. This makes us wonder if they’re really thinking or just copying what they learned. To figure this out, we did a small study to see how well these models can reason about abstract ideas. We found that they’re actually pretty bad at it! So, we made a special dataset and learning approach to help them do better. And it worked! They got much better at using general facts to answer questions and even started to understand things on their own. |
