Summary of Future Language Modeling From Temporal Document History, by Changmao Li and Jeffrey Flanigan
Future Language Modeling from Temporal Document History
by Changmao Li, Jeffrey Flanigan
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel task in machine learning, namely “future language modeling,” which involves predicting textual data based on a temporal history of texts. This task is particularly relevant as humans often make predictions in a textual format, whereas existing automated systems primarily focus on numerical data. The authors formalize this problem and show that it is possible to build future language models that outperform strong non-temporal baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting the future is important for many areas of human activity, such as business, trading, and technology. While there are many automated systems for predicting numbers, like weather or stock prices, there isn’t much work on predicting text. People often make predictions in writing, but computers haven’t been very good at doing this. The researchers introduce a new task called “future language modeling” that predicts texts based on past texts. They show it’s possible to build models that do better than previous ones. |
Keywords
» Artificial intelligence » Machine learning
