P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis

2025-08-06

AI Summary

Large language models (LLMs) often fail to simultaneously satisfy safety, helpfulness, and honesty when processing defective instructions—e.g., ambiguous, context-deficient, or tone-inappropriate prompts. Method: We propose P-Aligner, a *pre-decoding alignment* paradigm that lightweightly rewrites raw instructions prior to generation to better align with human preferences. Our approach introduces an instruction synthesis pipeline guided by Monte Carlo Tree Search (MCTS) and dual ethical/functional principles, enabling efficient construction of the high-quality UltraPrompt dataset—without end-to-end fine-tuning or costly inference-time search. The framework integrates preference modeling, structured exploration of the instruction space, and iterative deployment. Results: Experiments show average win-rate improvements of 28.35% on GPT-4-turbo and 8.69% on Gemma-2-SimPO over strong baselines, demonstrating superior performance with minimal inference overhead.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large Language Models (LLMs) are expected to produce safe, helpful, and honest content during interaction with human users, but they frequently fail to align with such values when given flawed instructions, e.g., missing context, ambiguous directives, or inappropriate tone, leaving substantial room for improvement along multiple dimensions. A cost-effective yet high-impact way is to pre-align instructions before the model begins decoding. Existing approaches either rely on prohibitive test-time search costs or end-to-end model rewrite, which is powered by a customized training corpus with unclear objectives. In this work, we demonstrate that the goal of efficient and effective preference alignment can be achieved by P-Aligner, a lightweight module generating instructions that preserve the original intents while being expressed in a more human-preferred form. P-Aligner is trained on UltraPrompt, a new dataset synthesized via a proposed principle-guided pipeline using Monte-Carlo Tree Search, which systematically explores the space of candidate instructions that are closely tied to human preference. Experiments across different methods show that P-Aligner generally outperforms strong baselines across various models and benchmarks, including average win-rate gains of 28.35% and 8.69% on GPT-4-turbo and Gemma-2-SimPO, respectively. Further analyses validate its effectiveness and efficiency through multiple perspectives, including data quality, search strategies, iterative deployment, and time overhead.

Problem

• Improving alignment of LLMs with human values via instruction pre-alignment
• Reducing prohibitive costs of existing alignment methods
• Generating human-preferred instructions while preserving original intent

Innovation

• Lightweight module for instruction pre-alignment
• UltraPrompt dataset via principle-guided synthesis
• Monte-Carlo Tree Search for human-preferred instructions