Summary of Logical Negation Augmenting and Debiasing For Prompt-based Methods, by Yitian Li et al.
Logical Negation Augmenting and Debiasing for Prompt-based Methods
by Yitian Li, Jidong Tian, Hao He, Yaohui Jin
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 paper explores the effectiveness of prompt-based methods in first-order logical reasoning, focusing on logical negation as a bottleneck. It finds that logical negation often results in spurious correlations to negative answers and proposes a simple method called Negation Augmenting and Negation Debiasing (NAND) to counteract this issue. NAND introduces negative propositions to prompt-based methods without updating parameters, allowing models to correctly reason logically. The paper demonstrates the effectiveness of NAND on three datasets, showing significant enhancements in logical reasoning capabilities without retraining the models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how good computers are at using hints (or prompts) to do logic problems. They found that when the hints have “not” or “no”, it’s hard for the computer to figure out what’s true and what’s false. To fix this, they created a new way called NAND (short for Negation Augmenting and Negation Debiasing). This makes the computers better at doing logic problems without having to relearn everything. They tested it on three different sets of data and saw big improvements. |
Keywords
» Artificial intelligence » Prompt
