Large Language Model's Multi-Capability Alignment in Biomedical Domain

2025-08-06

AI Summary

Addressing the challenge of aligning multiple capabilities—domain knowledge, reasoning, instruction following, and system integration—in biomedical large language models (LLMs), which often interfere with one another and lack sufficient clinical safety, this paper proposes BalancedBio. Our contributions are threefold: (1) the first formal Biomedical Multi-Capability Convergence Theorem; (2) a medical-knowledge-enhanced synthetic data generation method integrating clinical workflow constraints and ontology-based validation; and (3) capability-aware grouped relative policy optimization, combining rule-model hybrid reward shaping with Pareto-optimal analysis to ensure gradient orthogonality and clinical safety. Evaluated on a 0.5B-parameter model, BalancedBio achieves state-of-the-art performance: 80.95% domain knowledge accuracy, 61.94% reasoning score, 67.95% instruction-following fidelity, and 86.7% capability integration. In real-world deployment, it reduces operational costs by 78%, improves diagnostic accuracy by 23%, and attains 89% clinical acceptance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Medical and Biological Imaging

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

BalancedBio is a theoretically grounded framework for parameter-efficient biomedical reasoning, addressing multi-capability integration in domain-specific AI alignment. It establishes the Biomedical Multi-Capability Convergence Theorem, proving orthogonal gradient spaces are essential to prevent capability interference for safe deployment. Key innovations include: (1) Medical Knowledge Grounded Synthetic Generation (MKGSG), extending Source2Synth with clinical workflow constraints and medical ontology validation for factual accuracy and safety; and (2) Capability Aware Group Relative Policy Optimization, deriving optimal hybrid reward weighting to maintain orthogonality in RL, using a reward model with rule-based and model-based scores adapted to biomedical tasks. Mathematical analysis proves Pareto-optimal convergence, preserving performance across capabilities. It achieves state-of-the-art results in its parameter class: domain expertise (80.95% BIOMED-MMLU, +15.32% over baseline), reasoning (61.94%, +7.75%), instruction following (67.95%, +6.44%), and integration (86.7%, +18.5%). Theoretical safety guarantees include bounds on capability preservation and clinical accuracy. Real-world deployment yields 78% cost reduction, 23% improved diagnostic accuracy, and 89% clinician acceptance. This work provides a principled methodology for biomedical AI alignment, enabling efficient reasoning with essential safety and reliability, with the 0.5B model version to be released.

Problem

• Aligning multi-capabilities in biomedical domain-specific AI
• Preventing capability interference via orthogonal gradient spaces
• Ensuring factual accuracy and safety in biomedical reasoning

Innovation

• Medical Knowledge Grounded Synthetic Generation
• Capability Aware Group Relative Policy Optimization
• Biomedical Multi-Capability Convergence Theorem