DiARC Paper: Distinguishing Positive and Negative Samples Improves LLM Reasoning on ARC Tasks

A paper titled 'DiARC: Distinguishing Positive and Negative Samples Helps Improving ARC-like Reasoning Ability of Large Language Models' has been published on arXiv. The research focuses on the Abstraction and Reasoning Corpus (ARC) and proposes a method that enhances LLM reasoning by distinguishing positive and negative examples. Experiments show that this approach effectively helps models summarize patterns from limited grid samples and predict correct outputs.