Group Preference Optimization: Few-Shot Alignment of Large Language Models

• URL: http://arxiv.org/abs/2310.11523v2
• Date: Mon, 14 Oct 2024 20:02:11 GMT
• Title: Group Preference Optimization: Few-Shot Alignment of Large Language Models
• Authors: Siyan Zhao, John Dang, Aditya Grover,
• Abstract summary: Group Preference Optimization steers language models to preferences of individual groups in a few-shot manner. We empirically validate the efficacy of GPO through rigorous evaluations using large language models with varied sizes. Our results demonstrate that GPO not only aligns models more accurately but also requires fewer group-specific preferences, and less training and inference computing resources.
• Score: 28.464834028110538
• License:
• Abstract: Many applications of large language models (LLMs), ranging from chatbots to creative writing, require nuanced subjective judgments that can differ significantly across different groups. Existing alignment algorithms can be expensive to align for each group, requiring prohibitive amounts of group-specific preference data and computation for real-world use cases. We introduce Group Preference Optimization (GPO), an alignment framework that steers language models to preferences of individual groups in a few-shot manner. In GPO, we augment the base LLM with an independent transformer module trained to predict the preferences of a group for the LLM generations. For few-shot learning, we parameterize this module as an in-context autoregressive transformer and train it via meta-learning on several groups. We empirically validate the efficacy of GPO through rigorous evaluations using LLMs with varied sizes on three human opinion adaptation tasks. These tasks involve adapting to the preferences of US demographic groups, global countries, and individual users. Our results demonstrate that GPO not only aligns models more accurately but also requires fewer group-specific preferences, and less training and inference computing resources, outperforming existing strategies such as in-context steering and fine-tuning methods.

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