Summary of Ropo: Robust Preference Optimization For Large Language Models, by Xize Liang et al.
ROPO: Robust Preference Optimization for Large Language Models
by Xize Liang, Chao Chen, Shuang Qiu, Jie Wang, Yue Wu, Zhihang Fu, Zhihao Shi, Feng Wu, Jieping Ye
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 RObust Preference Optimization (ROPO) framework is an iterative alignment approach that tackles the issue of large language models (LLMs) generating helpful and harmless responses by addressing the problem of noise in preference data. ROPO integrates noise-tolerance and filtering of noisy samples without relying on external models, unlike existing methods that marginally alleviate or use costly teacher LLMs prone to reward misgeneralization. The framework iteratively solves a constrained optimization problem, assigning quality-aware weights to each sample while constraining the sum of the weights to the number of desired retained samples. Additionally, ROPO derives a robust loss by suppressing gradients for high-uncertainty samples, enabling noise-tolerant training and effective noise identification. This approach theoretically distinguishes noisy from clean samples and proposes a robustness-guided rejection sampling technique to compensate for discarded queries. Experimental results on three datasets with Mistral-7B and Llama-2-7B demonstrate ROPO’s superiority over existing methods as the noise rate increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper addresses a problem in language models, where they can sometimes generate harmful responses. To fix this, researchers developed a new approach called RObust Preference Optimization (ROPO). This method helps language models learn what is helpful and harmless by reducing noise in their training data. ROPO works by giving more importance to certain samples in the training data and less importance to others that might be noisy or unhelpful. The approach also helps identify which samples are noisy or unhelpful, so it can reject those and focus on the good ones. This makes the language models better at generating helpful responses. |
Keywords
* Artificial intelligence * Alignment * Llama * Optimization
