Summary of Towards More Effective Table-to-text Generation: Assessing In-context Learning and Self-evaluation with Open-source Models, by Sahar Iravani et al.
Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models
by Sahar Iravani, Tim .O .F Conrad
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Recent advancements in language models have significantly improved table processing, a key task in natural language processing. However, the capabilities of these models in table-to-text generation require further investigation. This study explores various in-context learning strategies in language models across benchmark datasets, focusing on the impact of providing examples to the model. The authors examine a real-world use case and offer valuable insights into practical applications. Additionally, they employ a large language model self-evaluation approach using chain-of-thought reasoning and assess its correlation with human-aligned metrics like BERTScore. The findings highlight the significant impact of examples in improving table-to-text generation and suggest that while LLM self-evaluation has potential, its current alignment with human judgment could be enhanced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well language models can turn structured data into clear text. They tried different ways to teach these models using real-world examples. The results show that providing examples really helps improve the model’s ability to create readable text from tables. The researchers also tested a new way of evaluating the model’s performance, which uses chain-of-thought reasoning. While this method has potential, it needs more work to match how humans evaluate language. |
Keywords
» Artificial intelligence » Alignment » Large language model » Natural language processing » Text generation
