Summary of A Critical Look at Tokenwise Reward-guided Text Generation, by Ahmad Rashid et al.
A Critical Look At Tokenwise Reward-Guided Text Generation
by Ahmad Rashid, Ruotian Wu, Julia Grosse, Agustinus Kristiadi, Pascal Poupart
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 novel approach to prediction-time tokenwise reward-guided text generation (RGTG) that alleviates the limitation of traditional fine-tuning methods for large language models (LLMs). Specifically, it trains a Bradley-Terry reward model on partial sequences and autoregressively samples from the implied tokenwise policy during decoding time. The authors show that this approach outperforms previous RGTG methods and achieves similar performance to strong offline baselines without requiring large-scale LLM fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve language models by making them better at following human preferences. It does this by training a special kind of model on partial text sequences, which then guides the generation of new text during decoding time. This approach is more efficient than traditional methods that require fine-tuning large language models, and it achieves similar results without needing as much computational power. |
Keywords
» Artificial intelligence » Fine tuning » Text generation
