Summary of Reasoning Abilities Of Large Language Models: In-depth Analysis on the Abstraction and Reasoning Corpus, by Seungpil Lee and Woochang Sim and Donghyeon Shin and Wongyu Seo and Jiwon Park and Seokki Lee and Sanha Hwang and Sejin Kim and Sundong Kim
Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus
by Seungpil Lee, Woochang Sim, Donghyeon Shin, Wongyu Seo, Jiwon Park, Seokki Lee, Sanha Hwang, Sejin Kim, Sundong Kim
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Symbolic Computation (cs.SC)
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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 approach uses the Abstraction and Reasoning Corpus (ARC) benchmark to evaluate Large Language Models’ inference abilities comprehensively. The novel method focuses on three key components from the Language of Thought Hypothesis (LoTH): Logical Coherence, Compositionality, and Productivity. While LLMs demonstrate some inference capabilities, they still significantly lag behind human-level reasoning in these aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces a new way to assess Large Language Models’ ability to reason and understand context. The researchers used the ARC benchmark and focused on three important areas: making logical sense, combining ideas, and being productive. They found that while LLMs can do some reasoning, they still have a long way to go before reaching human-level understanding. |
Keywords
» Artificial intelligence » Inference
