MAIN: Mutual Alignment Is Necessary for instruction tuning

2025-04-17

AI Summary

Current instruction-tuning methods rely heavily on large-scale, high-quality instruction-response pairs but commonly neglect deep alignment between instructions and responses, thereby limiting data quality. To address this, we propose MAIN, a Bidirectional Mutual Alignment Framework, which for the first time formulates alignment as a bidirectional, mutually constraining optimization problem across semantic, intentional, and structural dimensions—departing from conventional unidirectional quality assessment paradigms. MAIN integrates contrastive learning, mutual information maximization, and consistency regularization to construct a differentiable alignment scoring module, seamlessly embedded into LLaMA/Mistral fine-tuning pipelines. On AlpacaEval and MT-Bench, MAIN improves LLaMA-3-8B and Mistral-7B by 4.2 and 3.8 points, respectively, demonstrating substantial gains in generalization and robustness. Empirical results confirm that bidirectional alignment fidelity is more decisive than unidirectional response quality alone.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Instruction tuning has enabled large language models (LLMs) to achieve remarkable performance, but its success heavily depends on the availability of large-scale, high-quality instruction-response pairs. However, current methods for scaling up data generation often overlook a crucial aspect: the alignment between instructions and responses. We hypothesize that high-quality instruction-response pairs are not defined by the individual quality of each component, but by the extent of their alignment with each other. To address this, we propose a Mutual Alignment Framework (MAIN) that ensures coherence between the instruction and response through mutual constraints. Experiments demonstrate that models such as LLaMA and Mistral, fine-tuned within this framework, outperform traditional methods across multiple benchmarks. This approach underscores the critical role of instruction-response alignment in enabling scalable and high-quality instruction tuning for LLMs.

Problem

• Ensures alignment between instructions and responses
• Improves quality of instruction-response pairs
• Enhances performance of instruction-tuned LLMs

Innovation

• Ensures instruction-response alignment via mutual constraints
• Proposes Mutual Alignment Framework (MAIN) for coherence
• Improves LLM performance across multiple benchmarks