Summary of Investigating Low-cost Llm Annotation For~spoken Dialogue Understanding Datasets, by Lucas Druart (lia) et al.
Investigating Low-Cost LLM Annotation for~Spoken Dialogue Understanding Datasets
by Lucas Druart, Valentin Vielzeuf, Yannick Estève
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
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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 study explores the impact of Large Language Models on enhancing semantic representations in spoken Task-Oriented Dialogue systems. The authors reveal that these models can be fine-tuned to improve the quality of semantic representations, which are crucial for effective dialogue flows. The research also evaluates the knowledge captured by generated annotations and examines semi-automatic annotation implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spoken dialogue systems rely on semantic representations to understand user requests. However, current spoken dialogue datasets lack detailed semantic representations compared to textual datasets. This study aims to bridge this gap by improving spoken dialogue dataset semantic representations using Large Language Models. The authors fine-tune these models and assess their impact on the quality of generated annotations. |
