Summary of Conceptsearch: Towards Efficient Program Search Using Llms For Abstraction and Reasoning Corpus (arc), by Kartik Singhal et al.
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)
by Kartik Singhal, Gautam Shroff
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The Abstraction and Reasoning Corpus (ARC) presents a significant challenge to artificial intelligence, requiring broad generalization and few-shot learning capabilities that current deep learning methods struggle to achieve. To address this, the authors introduce ConceptSearch, a novel function-search algorithm that leverages large language models (LLMs) for program generation and employs a concept-based scoring method to guide the search efficiently. The algorithm evaluates programs based on their ability to capture the underlying transformation concept reflected in input-output examples, rather than simplistic pixel-based metrics like Hamming distance. Experimental results demonstrate the effectiveness of ConceptSearch, achieving significant performance improvements over direct prompting with GPT-4 and exhibiting up to 30% greater efficiency compared to Haming distance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Abstraction and Reasoning Corpus is a tough challenge for artificial intelligence. It requires machines to learn new things quickly and from few examples. Current AI methods are not good at this. To solve the problem, researchers developed an algorithm called ConceptSearch. It uses big language models like GPT-4 to generate programs and then scores them based on how well they understand what’s happening in the data. This is different from just measuring how close the program is to a correct answer. The new way of scoring helps the algorithm work better, with results that are up to 30% faster than before. |
Keywords
» Artificial intelligence » Deep learning » Few shot » Generalization » Gpt » Prompting
