Mixture-of-Instructions: Aligning Large Language Models via Mixture Prompting
Keywords: language model, alignment, supervised fine-tuning
TL;DR: To address the need for multi-task alignment in LLMs, the Mixture-of-Instructions (MoI) technique was developed, boosting alignment efficiency and generative performance. Tested on Qwen-7B-chat, MoI improved capabilities across various tasks.
Abstract: With the proliferation of large language models (LLMs), the comprehensive alignment of such models across multiple tasks has emerged as a critical area of research. Existing alignment methodologies primarily address single task, such as multi-turn dialogue, coding, mathematical problem-solving, and tool usage. However, AI-driven products that leverage language models usually necessitate a fusion of these abilities to function effectively in real-world scenarios. Moreover, the considerable computational resources required for proper alignment of LLMs underscore the need for a more robust, efficient, and encompassing approach to multi-task alignment, ensuring improved generative performance. In response to these challenges, we introduce a novel technique termed Mixture-of-Instructions (MoI), which employs a strategy of instruction packing combined with diverse system prompts to boost the alignment efficiency of language models. We have also compiled a diverse set of seven benchmark datasets to rigorously evaluate the alignment efficacy of the MoI-enhanced language model. Our methodology was applied to the open-source Qwen-7B-chat model, culminating in the development of Qwen-SFT-MoI. This enhanced model demonstrates significant advancements in generative capabilities across coding, mathematics, and tool use tasks.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 5536
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