Summary of Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic, by Nathaniel Weir et al.
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
by Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 recent advancements in language models have enabled new opportunities for structured reasoning with text. However, a lack of clear protocol for determining valid compositional entailment has hindered progress in this area. This issue leads to noisy datasets and limited performance gains by modern neuro-symbolic engines. The authors formulate a consistent approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. They find that their new dataset, RDTE (Recognizing Decompositional Textual Entailment), has higher internal consistency than prior datasets. Additionally, training an entailment classifier via knowledge distillation and employing it in an entailment tree reasoning engine improves both accuracy and proof quality, demonstrating the practical benefits of this advance for textual inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how computers can reason with text like humans do. Right now, computers can’t always make sense of what they read because they don’t have a clear way to figure out what parts of a sentence are important and how they relate to each other. The authors created a new method for labeling these relationships, which improves the accuracy of computer programs that try to understand text. This is an important step towards computers being able to have more human-like conversations with us. |
Keywords
» Artificial intelligence » Inference » Knowledge distillation
