Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction (2501.05336v1)

Abstract: The rapid advancement of LLMs has led to significant improvements in their capabilities, but also to increased concerns about their alignment with human values and intentions. Current alignment strategies, including adaptive training and inference-time methods, have demonstrated potential in this area. However, these approaches still struggle to balance deployment complexity and capability across various tasks and difficulties. In this work, we introduce the Streaming Distribution Induce Aligner (Stream Aligner), a novel alignment paradigm that combines efficiency with enhanced performance in various tasks throughout the generation process. Stream Aligner achieves dynamic sentence-level correction by using a small model to learn the preferences of the suffix sentence, iteratively correcting the suffix sentence output by the upstream model, and then using the corrected sentence to replace the suffix sentence in subsequent generations. Compared to Aligner, our experiments demonstrate that Stream Aligner reduces reliance on the capabilities of additional models, enhances the reasoning abilities of LLMs, and decreases latency during user interaction. Specifically, Stream Aligner-2B model has achieved an improvement of 76.1% in helpfulness, 36.0% in harmlessness on the tested Llama2-70B-chat model, and Stream Aligner-8B has achieved an improvement of 3.5% on the math ability of the tested Llama3-70B-Instruct model.

Stream Aligner: Efficient Sentence-Level Alignment via Distribution Induction

The rapid evolution of LLMs such as Llama3-70B has brought forth both unprecedented capabilities and increased concerns regarding their alignment with human values and intentions. Various alignment strategies, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), have been proposed. However, these methods are often computationally expensive and sensitive to parameters, particularly in reasoning tasks. In response to these challenges, the authors have introduced a new paradigm named the Streaming Distribution Induce Aligner (Stream Aligner), which aims to achieve efficient alignment through dynamic sentence-level correction during the inference process.

Overview and Methodology

The Stream Aligner represents a novel approach where a lightweight model performs iterative corrections on sentences generated by a larger, upstream LLM. This framework aims to balance deployment complexity and performance across varying tasks. The crux of Stream Aligner's methodology is leveraging a small model, fine-tuned to correct sentence-level outputs iteratively, thereby inducing desired distributions over the language output. This sequential correction process allows for the exploitation of the upstream model's latent capabilities while mitigating unintended behavior.

The training phase of Stream Aligner involves a preference dataset to fine-tune these small models to discern preferences between optimal and sub-optimal responses. During inference, the methodology involves sentence-level corrections where the Stream Aligner serves as a plug-and-play module, refining outputs until reaching an acceptable alignment level.

Key Results

The empirical results of Stream Aligner were substantiated through intensive evaluations on tasks related to helpfulness, harmlessness, and mathematical reasoning. Notably, utilizing the Stream Aligner-2B model resulted in a maximum improvement of 41.2% in helpfulness and a 36.0% increase in harmlessness for the Llama2-70B-chat model. For mathematical tasks, Stream Aligner-8B demonstrated a 3.5% enhancement in accuracy on the Llama3-70B-Instruct model.

Theoretical and Practical Implications

Theoretically, this work underscores the potential of dynamic, sentence-level correction mechanisms to significantly improve alignment without the extensive resource demands typical of larger models. It emphasizes the benefit of an overview between inference-time strategies and the incorporation of smaller, additional models to significantly reduce latency and computational burden.

Practically, the deployment of Stream Aligner could represent a pivotal shift in developing more practical and efficient AI systems, particularly in applications where alignment with nuanced human values is crucial. This model offers an efficient alternative to large-scale model retraining, ensuring seamless integration into existing AI pipelines without compromising on performance metrics.

Future Directions

Future developments in this area could explore further refinements in the Stream Aligner's methodology. This might include enhancements in the learning dynamics of preference-based tuning and broader evaluations across diverse linguistic and ethical scenarios. Additionally, there is potential for more granular, feature-based approaches to corrective feedback, which could further streamline the alignment process.

The balance between model capability and alignment fidelity remains a critical consideration, suggesting avenues for further research into optimizing this balance. Overall, Stream Aligner offers a promising direction for achieving efficient and effective alignment in increasingly complex LLMs.