Summary of Sumie: a Synthetic Benchmark For Incremental Entity Summarization, by Eunjeong Hwang et al.
SUMIE: A Synthetic Benchmark for Incremental Entity Summarization
by Eunjeong Hwang, Yichao Zhou, Beliz Gunel, James Bradley Wendt, Sandeep Tata
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel language model benchmark, SUMIE, is proposed to evaluate incremental entity summarization (IES) abilities in large language models (LLMs). The dataset addresses the lack of real-world challenges in existing datasets, featuring complex and nuanced data. It includes attributes, summaries, and paragraphs generated in sequence, ensuring high quality with an alignment between summaries and paragraphs exceeding 96%. State-of-the-art LLMs struggle to update summaries with F1 scores above 80.4%, demonstrating the dataset’s difficulty. The benchmark and evaluation metrics will be open-sourced to facilitate progress on IES tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new test for language models that can keep updating information about people, places, and things. Right now, there isn’t a good way to check how well these models do this task. To fix this, the researchers make a special dataset called SUMIE that shows real-world problems like getting associations wrong or not showing all the facts. This dataset is unique because it captures the complexity of real-life data. The team will share their work so others can help improve language models. |
Keywords
» Artificial intelligence » Alignment » Language model » Summarization
