Summary of Generating Diverse Negations From Affirmative Sentences, by Darian Rodriguez Vasquez et al.
Generating Diverse Negations from Affirmative Sentences
by Darian Rodriguez Vasquez, Afroditi Papadaki
First submitted to arxiv on: 30 Oct 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 proposed NegVerse method tackles the lack of negation datasets by generating a diverse range of negation types from affirmative sentences. This is achieved through new rules for masking parts of sentences where negations are most likely to occur, based on syntactic structure, using a frozen baseline LLM and prompt tuning. The authors also propose a filtering mechanism to identify negation cues and remove degenerate examples, producing meaningful perturbations. NegVerse outperforms existing methods in terms of lexical similarity to the original sentences, syntactic preservation, and negation diversity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models struggle with reasoning under negated statements. To address this issue, a new dataset is created that includes diverse types of negations from affirmative sentences. The method uses rules based on sentence structure and prompt tuning to generate these negations. It also filters out poor examples to ensure the generated negations are meaningful. This approach leads to better performance compared to existing methods. |
Keywords
» Artificial intelligence » Prompt
