Summary of Talking the Talk Does Not Entail Walking the Walk: on the Limits Of Large Language Models in Lexical Entailment Recognition, by Candida M. Greco et al.
Talking the Talk Does Not Entail Walking the Walk: On the Limits of Large Language Models in Lexical Entailment Recognition
by Candida M. Greco, Lucio La Cava, Andrea Tagarelli
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Information Retrieval (cs.IR); Physics and Society (physics.soc-ph)
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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 investigates the capabilities of eight Large Language Models (LLMs) in recognizing lexical entailment relations among verbs through differently devised prompting strategies and zero-/few-shot settings. The models are tested over verb pairs from two lexical databases, WordNet and HyperLex. The findings show that LLMs can tackle the task with moderately good performance, but at varying levels of effectiveness under different conditions. Few-shot prompting is found to enhance model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well large language models can understand relationships between verbs in sentences. It uses two databases and tests different ways of asking the models to recognize these relationships. The results show that while the models are pretty good, they’re not perfect and still have room for improvement. |
Keywords
* Artificial intelligence * Few shot * Prompting
