Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing

• URL: http://arxiv.org/abs/2502.17282v1
• Date: Mon, 24 Feb 2025 16:10:53 GMT
• Title: Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing
• Authors: Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye,
• Abstract summary: Large Language Models (LLMs) have demonstrated human-like instruction-following abilities.<n>In this work, we explore how to route the best-performing LLM for each instruction to achieve better overall performance.<n>We develop a new paradigm, constructing capability instructions with model capability representation, user instruction, and performance inquiry prompts to assess the performance.
• Score: 64.38277118982698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated human-like instruction-following abilities, particularly those exceeding 100 billion parameters. The combined capability of some smaller, resource-friendly LLMs can address most of the instructions that larger LLMs excel at. In this work, we explore how to route the best-performing LLM for each instruction to achieve better overall performance. We develop a new paradigm, constructing capability instructions with model capability representation, user instruction, and performance inquiry prompts to assess the performance. To learn from capability instructions, we introduce a new end-to-end framework called Model Selection with Aptitude Test (Model-SAT), which generates positive and negative samples based on what different models perform well or struggle with. Model-SAT uses a model capability encoder that extends its model representation to a lightweight LLM. Our experiments show that Model-SAT understands the performance dimensions of candidate models and provides the probabilities of their capability to handle various instructions. Additionally, during deployment, a new model can quickly infer its aptitude test results across 50 tasks, each with 20 shots. Model-SAT performs state-of-the-art model routing without candidate inference and in real-world new model-released scenarios. The code is available at https://github.com/Now-Join-Us/CIT-LLM-Routing

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