Summary of Thought-path Contrastive Learning Via Premise-oriented Data Augmentation For Logical Reading Comprehension, by Chenxu Wang et al.
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension
by Chenxu Wang, Ping Jian, Zhen Yang
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper tackles the challenging task of logical reading comprehension, which requires understanding text semantics and applying reasoning to deduce correct answers. Previous work has focused on enhancing logical reasoning through Chain-of-Thought (CoT) or data augmentation methods. However, these approaches have limitations, such as neglecting incorrect alternatives in CoT rationales and relying on rule-based methods for data augmentation. The proposed Premise-Oriented Data Augmentation (PODA) framework addresses these issues by generating CoT rationales that include analyses of both correct and incorrect options, while constructing diverse and high-quality counterfactual contexts from incorrect candidate options. The PODA framework integrates summarizing premises and identifying premises for each option into rationales. To facilitate the model’s capabilities to better differentiate the reasoning process associated with each option, a novel thought-path contrastive learning method is introduced that compares reasoning paths between original and counterfactual samples. Experimental results on three representative language models demonstrate that this method can improve baselines substantially across two challenging logical reasoning benchmarks (ReClor and LogiQA 2.0). Keywords: logical reading comprehension, Chain-of-Thought, Premise-Oriented Data Augmentation, thought-path contrastive learning, ReClor, LogiQA 2.0. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help machines understand text better. Right now, machines have trouble understanding why certain answers are correct or incorrect. The researchers propose a new method called Premise-Oriented Data Augmentation (PODA) that helps machines generate explanations for both correct and incorrect answers. This is important because it allows the machine to learn from its mistakes and improve over time. The PODA method creates diverse and high-quality counterfactual contexts, which are like fake scenarios that help the machine practice reasoning. The researchers tested their method on three language models and found that it improved performance on two challenging text comprehension benchmarks. In simple terms, this paper is about making machines better at understanding and explaining text. It has important implications for artificial intelligence and could be used in applications such as customer service chatbots or language translation software. |
Keywords
» Artificial intelligence » Data augmentation » Semantics » Translation
