A Systematic Examination of Preference Learning through the Lens of Instruction-Following (2412.15282v1)

Abstract: Preference learning is a widely adopted post-training technique that aligns LLMs to human preferences and improves specific downstream task capabilities. In this work we systematically investigate how specific attributes of preference datasets affect the alignment and downstream performance of LLMs in instruction-following tasks. We use a novel synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with combinations of 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) - to obtain pairs of (chosen, rejected) responses. Then, we perform experiments investigating the effects of (1) the presence of shared prefixes between the chosen and rejected responses, (2) the contrast and quality of the chosen, rejected responses and (3) the complexity of the training prompts. Our experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements and greater stability across challenging training configurations. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance by balancing diversity and learning efficiency. Additionally, training on prompts of moderate difficulty leads to better generalization across tasks, even for more complex evaluation scenarios, compared to overly challenging prompts. Our findings provide actionable insights into optimizing preference data curation for instruction-following tasks, offering a scalable and effective framework for enhancing LLM training and alignment.

• The paper introduces a novel synthetic data pipeline that generates 48,000 unique instruction-following prompts for evaluating LLM performance.
• It assesses two data curation methods, RS and MCTS, to create effective preference pairs with shared prefixes and balanced contrast quality.
• Moderately challenging prompts yielded better model generalization, highlighting the importance of balancing prompt difficulty in training.

A Systematic Examination of Preference Learning through the Lens of Instruction-Following

The paper presents a comprehensive analysis of preference learning mechanisms for LLMs, specifically focusing on instruction-following tasks. Preference learning is essential in aligning LLM outputs with human expectations, a significant challenge in the development of AI systems that can understand and follow complex instructions.

Key Contributions

The focal point of this research is the synthesis of instructional prompts and their subsequent evaluation using preference pairs. This paper introduces a novel synthetic data generation pipeline that enables the creation of 48,000 unique instruction-following prompts. These are evaluated under 23 verifiable constraints, allowing fine-grained assessment of model responses.

The paper explores two data curation methods: Rejection Sampling (RS) and Monte Carlo Tree Search (MCTS). These methods are used to determine the preference pairs by evaluating chosen and rejected responses. The research critically examines three pivotal dimensions of preference datasets:

1. Shared Prefixes in Preference Pairs: The impact of having structural consistency between chosen and rejected responses.
2. Contrast and Quality of Responses: The significance of high-contrast versus low-contrast pairing.
3. Difficulty of Training Prompts: The influence of prompt complexity on model generalization.

Insights and Findings

1. Shared Prefixes: It was found that preference pairs with shared prefixes, as generated by MCTS, offer marginal yet consistent improvements. This regularity offers greater stability across diverse training scenarios compared to RS, which does not incorporate shared prefixes.
2. Contrast and Quality Balance: High-contrast preference pairs were generally more effective than low-contrast pairs, but a combination of both produced superior performance outcomes, suggesting a balance between diversity and learning efficiency.
3. Training Prompt Complexity: Moderately difficult prompts led to better generalization across tasks, outperforming models trained on more complex prompts. This demonstrates that overwhelming difficulty in training data may hinder learning efficacy and model capability to generalize to varied scenarios.

Implications and Future Directions

The implications of this work are two-fold. Practically, it provides a methodology to enhance LLM alignment with human preferences, thus producing more reliable and agreeable AI systems. Theoretically, the paper extends the understanding of how different attributes in preference datasets can affect learning outcomes.

The paper opens several pathways for future research, particularly in exploring the scalability of these methods in real-world applications, where instruction-following involves a broader spectrum of constraints and complexities. Furthermore, development in adaptive curation techniques that dynamically adjust prompt difficulty and preference pair attributes could optimize learning and performance in LLMs.

Ultimately, this research contributes significant insights into preference learning frameworks, supporting the evolution of more nuanced and intelligently aligned LLMs in AI.