Summary of Intelligence Analysis Of Language Models, by Liane Galanti and Ethan Baron
Intelligence Analysis of Language Models
by Liane Galanti, Ethan Baron
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This research project assesses the abilities of Large Language Models (LLMs) on the Abstraction and Reasoning Corpus (ARC) dataset, a benchmark for abstract reasoning tasks. The ARC dataset includes prompts that require object identification, basic counting, and elementary geometric principles. Initially, LLMs are evaluated through a Zero-shot approach. Then, the Chain-of-Thought (CoT) technique is applied to improve model performance. Results show that despite high expectations, contemporary LLMs struggle in non-linguistic domains, even with simpler ARC subsets. This study is the first to focus on open-source models’ capabilities in this context. The project’s code, dataset, and prompts are available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well Large Language Models (LLMs) can do tasks that require abstract thinking. They used a special set of problems called the Abstraction and Reasoning Corpus (ARC). These problems need you to understand things like objects, counting, and basic shapes. The LLMs didn’t do as well as expected on these tasks, even when they were given simpler problems from ARC. This is the first study that looked at how open-source LLMs can do abstract thinking. |
Keywords
» Artificial intelligence » Zero shot
