Beyond the Binary: Capturing Diverse Preferences With Reward Regularization (2412.03822v1)

Abstract: LLMs are increasingly deployed via public-facing interfaces to interact with millions of users, each with diverse preferences. Despite this, preference tuning of LLMs predominantly relies on reward models trained using binary judgments where annotators select the preferred choice out of pairs of model outputs. In this work, we argue that this reliance on binary choices does not capture the broader, aggregate preferences of the target user in real-world tasks. We propose a taxonomy that identifies two dimensions of subjectivity where different users disagree on the preferred output-namely, the Plurality of Responses to Prompts, where prompts allow for multiple correct answers, and the Indistinguishability of Responses, where candidate outputs are paraphrases of each other. We show that reward models correlate weakly with user preferences in these cases. As a first step to address this issue, we introduce a simple yet effective method that augments existing binary preference datasets with synthetic preference judgments to estimate potential user disagreement. Incorporating these via a margin term as a form of regularization during model training yields predictions that better align with the aggregate user preferences.

• The paper demonstrates that integrating margin-based reward regularization with synthetic judgments significantly improves LLM alignment with heterogeneous user preferences.
• It critiques binary preference tuning by highlighting weak correlations in subjectivity dimensions such as response plurality and indistinguishability.
• Experimental results using a DeBERTa-V3 model evidenced robust performance gains across diverse datasets and real-world scenarios.

Analyzing Diverse Preferences in LLMs: Reward Regularization

The paper "Beyond the Binary: Capturing Diverse Preferences With Reward Regularization" addresses a crucial issue in the deployment of LLMs, particularly regarding the alignment of these models to the varied preferences of users across different socio-cultural backgrounds. The authors critique the prevalent methodology of preference tuning, which relies heavily on reward models trained using binary judgments, suggesting that this approach inadequately captures the complexity and diversity of human preferences.

Background and Context

LLMs are increasingly embedded in numerous applications, interacting with diverse user bases that possess heterogeneous preferences and linguistic backgrounds. Standard approaches for fine-tuning these models involve utilizing preference judgments, primarily in the form of binary choices between pairs of model outputs. While computationally efficient, this binary method simplifies the rich variability of user preferences, often leading to misalignment when applied in real-world scenarios where multiple equally valid responses may exist or where user responses may be indistinguishable from one another due to paraphrasing.

Key Contributions

The authors propose a nuanced taxonomy identifying two dimensions of subjectivity that present challenges to current reward models: the Plurality of Responses to Prompts and the Indistinguishability of Responses. They provide empirical evidence suggesting that existing reward models exhibit weak correlations with user preferences in these dimensions, particularly with subjective content where multiple correct answers are plausible.

In response, a novel methodology incorporating synthetic preference judgments is introduced. This involves enhancing traditional binary preference datasets with synthetic annotations generated via LLMs to estimate potential user disagreement. By introducing a margin term into the reward model training process, their method aims to regularize the predictions, aligning them better with the aggregate user preferences of a hypothetical population.

Experimental Validation and Insights

The researchers conduct extensive experiments using a trained DeBERTa-V3 reward model, analyzing how their regularization method influences performance across several datasets. Results highlight that the proposed regularization adjustment notably improves alignment with user preferences on datasets both within and outside of the training domain, particularly in cases where the prompts admit multiple correct responses.

The results suggest that this enhancement leads to more reliable and user-aligned reward models, which better accommodate subjective user preferences without depending on large-scale, costly re-annotation of data by humans.

Implications and Future Directions

This paper makes significant strides in bridging the gap between user diversity and model alignment. By moving toward models that account for a broader distribution of preferences, there is the potential to develop LLMs that are more representative of global user bases. This method's application extends beyond merely improving the performance of current models but also opens avenues for other normative considerations in AI alignment like distributive justice.

Future work may focus on refining synthetic preference generation techniques, exploring richer models for user preference distribution, and integrating these advances into personalization algorithms to cater to individual or group-specific needs. Additionally, ethical considerations related to the use of synthetic data for alignment purposes warrant further exploration to mitigate risks of bias or misrepresentation.

In conclusion, this paper provides a thoughtful examination of the limitations in current LLM alignment paradigms, offering practical enhancements through reward regularization while suggesting a path towards more socially inclusive AI systems.