Summary of Learning to Plan Long-term For Language Modeling, by Florian Mai et al.
Learning to Plan Long-Term for Language Modeling
by Florian Mai, Nathan Cornille, Marie-Francine Moens
First submitted to arxiv on: 23 Aug 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 planner improves the accuracy of next-token predictions in language models by introducing a latent plan that forecasts many sentences into the future. By sampling multiple plans simultaneously, the model conditions its predictions on an accurate estimate of text continuations, allowing for better token prediction and trading computation time for improved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach allows language models to spend more computational resources planning long-distance future text, leading to better next-token predictions. The planner uses a powerful function like attention to consider past text and predict a latent plan for many sentences into the future, enabling more accurate predictions. |
Keywords
* Artificial intelligence * Attention * Token
