Robust Preference Optimization with Provable Noise Tolerance for Training Large Language Models

Conceitos essenciais

The key idea of ROPO is to dynamically assign conservative gradient weights to response pairs with high label uncertainty, based on the log-likelihood margins between the responses. This weighting strategy effectively suppresses the gradients of noisy samples and ensures that the expected risk maintains the same gradient direction under both noisy and noise-free conditions.

Resumo

The paper proposes a provably noise-tolerant preference alignment method, called RObust Preference Optimization (ROPO), to mitigate the adverse effects of label noise on the preference alignment of large language models (LLMs).
The key insights are:

Existing ranking-based preference alignment methods, such as Direct Preference Optimization (DPO), are sensitive to label noise in the preference data. The noise can significantly degrade their performance.

ROPO addresses this issue by dynamically assigning conservative gradient weights to response pairs with high label uncertainty. Specifically, the weight function is an even function with respect to the log-likelihood margin between the responses. The more the margin diverges from the preference label, the smaller the gradient weight ROPO assigns.

This weighting strategy not only suppresses the gradients of noisy samples, but its symmetry also ensures that the expected risk maintains the same gradient direction under both noisy and noise-free conditions.

Theoretical analysis shows that under mild assumptions, the expected risk of the ROPO objective under a noisy preference data distribution shares the same optimal solution as that under the clean preference data distribution.

Experiments on three open-ended text generation tasks with four base models ranging in size from 2.8B to 13B demonstrate that ROPO significantly outperforms existing ranking-based methods, especially under noisy preference data.

Estatísticas

The label noise greatly affects the win rate of DPO against the chosen responses and the SFT model on the Anthropic-HH and TL;DR datasets. The win rate decreases by at least 14% under 20% noisy labels.
The reward margins in the noisy data are significantly higher than those on the clean data, which confirms the soundness of the noise-tolerant gradient weighting in ROPO.

Citações

"The preference alignment aims to enable large language models (LLMs) to generate responses that conform to human values, which is essential for developing general AI systems."
"Ranking-based methods—a promising class of alignment approaches—learn human preferences from datasets containing response pairs by optimizing the log-likelihood margins between preferred and dis-preferred responses."
"To address this problem, we propose a provably noise-tolerant preference alignment method, namely RObust Preference Optimization (ROPO)."

Principais Insights Extraídos De

Robust Preference Optimization with Provable Noise Tolerance for LLMs

by Xize Liang,C... às arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04102.pdf

Perguntas Mais Profundas

How can the noise-tolerant gradient weighting in ROPO be extended to other preference learning tasks beyond text generation, such as image or video generation?

In order to extend the noise-tolerant gradient weighting technique used in ROPO to other preference learning tasks like image or video generation, we need to adapt the concept to the specific characteristics of these domains. Here are some ways this extension can be achieved:

Feature Representation: For image or video generation tasks, the features extracted from the data play a crucial role. The noise-tolerant gradient weighting can be applied to the features extracted from images or video frames instead of text tokens. By dynamically assigning conservative gradient weights based on the uncertainty in the feature representations, the model can learn to be robust to noisy labels.

Loss Function Design: The design of the loss function in preference learning tasks for images or videos needs to consider the specific characteristics of these data types. The noise-tolerant gradient weighting can be incorporated into the loss function to penalize noisy samples effectively. This can help the model focus on learning from reliable data points while mitigating the impact of noisy labels.

Model Architecture: The noise-tolerant gradient weighting technique can be integrated into the training process of neural networks designed for image or video generation tasks. By adjusting the gradient weights based on the uncertainty in the preference labels, the model can learn to generate outputs that align with human preferences even in the presence of noise.

Evaluation and Validation: Extending the noise-tolerant gradient weighting to image or video generation tasks also requires careful evaluation and validation. It is essential to assess the model's performance on generating outputs that align with human preferences, considering the specific evaluation metrics relevant to image or video quality.

By adapting the noise-tolerant gradient weighting technique in ROPO to the unique characteristics of image or video data, it is possible to enhance the robustness of preference learning models in these domains.

What are the potential limitations of ROPO, and how can they be addressed in future research?

While ROPO offers significant advantages in mitigating the impact of noisy labels in preference learning tasks, there are some potential limitations that need to be addressed in future research:

Generalization to Diverse Datasets: One limitation of ROPO is its generalization to diverse datasets with varying levels of noise. Future research could focus on enhancing the adaptability of ROPO to different data distributions and noise levels, ensuring its effectiveness across a wide range of preference learning tasks.

Scalability to Large-Scale Models: As preference learning tasks often involve large-scale models, the scalability of ROPO to such models can be a challenge. Future research could explore optimization techniques and parallel processing methods to improve the scalability of ROPO for training on massive datasets and models.

Interpretability and Explainability: The interpretability of the noise-tolerant gradient weighting in ROPO may pose challenges in understanding how the model assigns weights to different samples. Future research could focus on enhancing the explainability of ROPO, providing insights into the decision-making process of the model.

Robustness to Adversarial Attacks: Adversarial attacks targeting preference learning models could potentially exploit the noise-tolerant gradient weighting in ROPO. Future research could investigate techniques to enhance the robustness of ROPO against adversarial manipulations and ensure the model's reliability in real-world scenarios.

By addressing these limitations through further research and development, ROPO can continue to advance the field of preference alignment and contribute to the development of more robust and reliable AI systems.

Given the importance of preference alignment for developing safe and ethical AI systems, how can the insights from ROPO be applied to other areas of AI safety and robustness?

The insights from ROPO can be valuable for enhancing AI safety and robustness in various areas beyond preference alignment. Here are some ways these insights can be applied:

Robust Training Procedures: The noise-tolerant gradient weighting technique in ROPO can be adapted to improve the robustness of AI models in safety-critical applications such as autonomous vehicles or medical diagnosis. By dynamically adjusting the model's learning process based on the uncertainty in the data, AI systems can be trained to make more reliable decisions in real-world scenarios.

Adversarial Defense Mechanisms: The principles of noise tolerance and robust optimization in ROPO can be leveraged to develop defense mechanisms against adversarial attacks in AI systems. By incorporating similar techniques to mitigate the impact of adversarial perturbations, AI models can become more resilient to malicious manipulations.

Ethical Decision-Making: AI systems often face ethical dilemmas in decision-making processes. The insights from ROPO can guide the development of AI algorithms that align with ethical principles and human values. By incorporating noise-tolerant techniques, AI systems can make decisions that are more aligned with societal norms and ethical standards.

Fairness and Bias Mitigation: Preference alignment techniques like ROPO can also be applied to address issues of fairness and bias in AI systems. By considering the uncertainty in data labels and dynamically adjusting the model's learning process, AI systems can mitigate biases and ensure fair treatment across different demographic groups.

By applying the insights from ROPO to these areas of AI safety and robustness, researchers and practitioners can advance the development of AI systems that are not only technically proficient but also safe, ethical, and reliable in diverse real-world applications.