Summary of A Critical Evaluation Of Ai Feedback For Aligning Large Language Models, by Archit Sharma et al.
A Critical Evaluation of AI Feedback for Aligning Large Language Models
by Archit Sharma, Sedrick Keh, Eric Mitchell, Chelsea Finn, Kushal Arora, Thomas Kollar
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper questions whether the complexity of reinforcement learning (RL) is truly warranted in improving instruction-following abilities of powerful pre-trained language models. The authors argue that recent improvements are largely due to using a weaker teacher model for supervised fine-tuning and then reinforcing it with RL from a critic model. By comparing simple supervised fine-tuning with a stronger teacher model, the paper shows that this approach outperforms existing RLAIF pipelines. Additionally, the authors explore how gains vary across different base models, evaluation protocols, and critic models, providing insights for making RLAIF more effective in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make language models better follow instructions. Right now, a popular way is called Reinforcement Learning with AI Feedback (RLAIF). This process takes a strong teacher model, fine-tunes it using demonstrations, and then uses feedback from another critic model to improve even more. But the authors ask if this extra step is really necessary. They show that just using the stronger teacher model for fine-tuning can get better results than the whole RLAIF process. The paper also looks at how different models and ways of testing affect the results. |
Keywords
* Artificial intelligence * Fine tuning * Reinforcement learning * Supervised * Teacher model
