Summary of Sign Of the Times: Evaluating the Use Of Large Language Models For Idiomaticity Detection, by Dylan Phelps et al.
Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
by Dylan Phelps, Thomas Pickard, Maggie Mi, Edward Gow-Smith, Aline Villavicencio
First submitted to arxiv on: 15 May 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 abstract presents a study that investigates the performance of large language models (LLMs) on idiomaticity tasks compared to encoder-only models fine-tuned specifically for those tasks. The authors evaluate several LLMs on three datasets: SemEval 2022 Task 2a, FLUTE, and MAGPIE. While LLMs show competitive performance, they do not surpass the results of task-specific models, even at large scales like GPT-4. However, model scale is found to consistently improve performance. The authors also explore prompting approaches to enhance performance and discuss the practical applications of using LLMs for idiomaticity tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well big language models do on understanding idioms and expressions. Unlike previous models that were trained specifically for this task, these big models don’t perform as well. However, they do get better when they’re larger. The researchers also tried different ways to help the models understand idioms better. They found out that even though the big models aren’t perfect, they can still be useful for certain tasks. |
Keywords
» Artificial intelligence » Encoder » Gpt » Prompting
