Summary of End-to-end Planner Training For Language Modeling, by Nathan Cornille et al.
End-to-end Planner Training for Language Modeling
by Nathan Cornille, Florian Mai, Jingyuan Sun, Marie-Francine Moens
First submitted to arxiv on: 16 Oct 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 This paper proposes a method to enhance language modeling for various tasks, such as natural language processing and text generation. The approach uses a separate planning module to predict abstract labels of future sentences and conditions the language model (LM) on these predictions. However, this method is non-differentiable, preventing joint end-to-end tuning of the planner with the LM. To overcome this limitation, the authors propose an effective way to approximate the gradient of selecting a label using predicted label probabilities as mixing weights. This enables joint fine-tuning of the planner and the LM while retaining more information from the full label distribution. Experimental results show consistent improvements in perplexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand human language better. It’s like teaching a computer to predict what someone will say next based on what they’ve said before. The current method is good, but it has a problem: it can’t be fine-tuned with the main computer program that does the prediction. The authors found a way to fix this by using probabilities as weights to mix in more information from the predicted labels. This makes the language model better at understanding human language and produces more accurate results. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Natural language processing » Perplexity » Text generation
