REAL: Response Embedding-based Alignment for LLMs

• URL: http://arxiv.org/abs/2409.17169v4
• Date: Wed, 04 Jun 2025 15:32:37 GMT
• Title: REAL: Response Embedding-based Alignment for LLMs
• Authors: Honggen Zhang, Xufeng Zhao, Igor Molybog, June Zhang,
• Abstract summary: We propose a strategy for constructing a high-quality training dataset that focuses on acquiring the less ambiguous preference pairs.<n>Experiments show that choosing dissimilar response pairs enhances the direct alignment of LLMs.<n>Findings suggest that focusing on distinct pairs can reduce the label error and improve LLM alignment efficiency.
• Score: 1.9513983244114355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aligning large language models (LLMs) to human preferences is a crucial step in building helpful and safe AI tools, which usually involve training on supervised datasets. Popular algorithms such as Direct Preference Optimization (DPO) rely on pairs of AI-generated responses ranked according to human annotation. The response pair annotation process might bring human bias. Building a correct preference dataset is the costly part of the alignment pipeline. To improve annotation efficiency and quality in the LLMs alignment, we propose REAL: Response Embedding-based Alignment for LLMs, a strategy for constructing a high-quality training dataset that focuses on acquiring the less ambiguous preference pairs for labeling out of a set of response candidates. Our selection process is based on the similarity of embedding responses independently of prompts, which guarantees the selection process in an off-policy setting, avoiding adaptively measuring the similarity during the training. Experimental results on real-world dataset SHP2 and synthetic HH-RLHF benchmarks indicate that choosing dissimilar response pairs enhances the direct alignment of LLMs while reducing inherited labeling errors. The model aligned with dissimilar response pairs obtained a better margin and win rate on the dialogue task. Our findings suggest that focusing on distinct pairs can reduce the label error and improve LLM alignment efficiency, saving up to $65\%$ of annotators' work.

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