Summary of Chew: a Dataset Of Changing Events in Wikipedia, by Hsuvas Borkakoty et al.
CHEW: A Dataset of CHanging Events in Wikipedia
by Hsuvas Borkakoty, Luis Espinosa-Anke
First submitted to arxiv on: 27 Jun 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 The proposed dataset, CHEW, is a novel collection of naturally occurring text describing changing events in Wikipedia. This dataset is used to evaluate the timeline understanding abilities of large language models (LLMs) in generative and classification tasks. The results indicate that LLMs, despite having access to temporal information, struggle to create accurate timelines. Additionally, the study demonstrates the effectiveness of CHEW-derived embeddings for identifying changes in meaning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called CHEW, which contains text about changing events on Wikipedia. They use this dataset to test how well large language models can understand and generate timelines about these events. The results show that these models are not very good at creating accurate timelines. However, the study also shows that the embeddings from this dataset can be useful for detecting changes in meaning. |
Keywords
* Artificial intelligence * Classification
