Summary of Training Llms to Recognize Hedges in Spontaneous Narratives, by Amie J. Paige et al.
Training LLMs to Recognize Hedges in Spontaneous Narratives
by Amie J. Paige, Adil Soubki, John Murzaku, Owen Rambow, Susan E. Brennan
First submitted to arxiv on: 6 Aug 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 This paper explores the concept of “hedges” in human language, specifically in an experimentally parameterized corpus of 63 Roadrunner cartoon narratives. Hedges are linguistic markers that indicate provisional or uncertain statements, and can signal non-prototypicality, lack of commitment, attribution of responsibility, invitation for input, or softening critical feedback. The authors created a gold standard of hedges annotated by human coders (Roadrunner-Hedge corpus) and tested three Large Language Model (LLM)-based approaches for hedge detection: fine-tuning BERT, zero-shot prompting with GPT-4o, and few-shot prompting with LLaMA-3. The best-performing approach was a fine-tuned BERT model, followed by few-shot GPT-4o. The authors analyzed errors in the top-performing approaches to improve the gold standard coding and highlight ambiguous cases that will guide future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how people use special language clues called “hedges” when talking to each other. Hedges help make statements less certain or softer, like saying something is “kind of true” instead of just saying it’s true. The researchers analyzed a big collection of stories from the cartoon show Roadrunner and found three ways that computers can be taught to recognize these special language clues. They tested different methods and found that one method worked better than others. This study will help us understand how people use language and how we can teach computers to understand it too. |
Keywords
» Artificial intelligence » Bert » Few shot » Fine tuning » Gpt » Large language model » Llama » Prompting » Zero shot
