Summary of Datatales: a Benchmark For Real-world Intelligent Data Narration, by Yajing Yang et al.
DataTales: A Benchmark for Real-World Intelligent Data Narration
by Yajing Yang, Qian Liu, Min-Yen Kan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel benchmark called DataTales is introduced to evaluate the proficiency of language models in transforming complex tabular data into accessible narratives. Existing benchmarks are limited in capturing the required analytical complexity for practical applications. DataTales addresses this gap by offering 4.9k financial reports paired with corresponding market data, showcasing the demand for models to create clear narratives and analyze large datasets while understanding specialized terminology. The findings highlight the significant challenge that language models face in achieving precision and analytical depth for proficient data narration, suggesting promising avenues for future model development and evaluation methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DataTales is a new way to test how well language models can take complex data and turn it into easy-to-understand stories. Right now, most tests don’t challenge the models enough to make them good at this task. DataTales fixes this by giving the models 4.9k financial reports to work with, along with the market data that goes with each report. This shows how important it is for the models to create clear stories and understand special terms in a field like finance. |
Keywords
» Artificial intelligence » Precision
