Process-Supervised LLM Recommenders via Flow-guided Tuning

While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness. To address this, we present Flow-guided fine-tuning recommender (Flower), which replaces SFT with a Generative Flow Network (GFlowNet) framework that enacts process supervision through token-level reward propagation. Flower's key innovation lies in decomposing item-level rewards into constituent token rewards, enabling direct alignment between token generation probabilities and their reward signals. This mechanism achieves three critical advancements: (1) popularity bias mitigation and fairness enhancement through empirical distribution matching, (2) preservation of diversity through GFlowNet's proportional sampling, and (3) flexible integration of personalized preferences via adaptable token rewards. Experiments demonstrate Flower's superior distribution-fitting capability and its significant advantages over traditional SFT in terms of fairness, diversity, and accuracy, highlighting its potential to improve LLM-based recommendation systems. The implementation is available via https://github.com/Mr-Peach0301/Flower

Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation Tasks

Medication recommendation systems have garnered attention within healthcare for their potential to deliver personalized and efficacious drug combinations based on patient's clinical data. However, existing methodologies encounter challenges in adapting to diverse Electronic Health Records (EHR) systems and effectively utilizing unstructured data, resulting in limited generalization capabilities and suboptimal performance. Recently, interest is growing in harnessing Large Language Models (LLMs) in the medical domain to support healthcare professionals and enhance patient care. Despite the emergence of medical LLMs and their promising results in tasks like medical question answering, their practical applicability in clinical settings, particularly in medication recommendation, often remains underexplored. In this study, we evaluate both general-purpose and medical-specific LLMs for medication recommendation tasks. Our findings reveal that LLMs frequently encounter the challenge of overprescribing, leading to heightened clinical risks and diminished medication recommendation accuracy. To address this issue, we propose Language-Assisted Medication Recommendation (LAMO), which employs a parameter-efficient fine-tuning approach to tailor open-source LLMs for optimal performance in medication recommendation scenarios. LAMO leverages the wealth of clinical information within clinical notes, a resource often underutilized in traditional methodologies. As a result of our approach, LAMO outperforms previous state-of-the-art methods by over 10% in internal validation accuracy. Furthermore, temporal and external validations demonstrate LAMO's robust generalization capabilities across various temporal and hospital contexts. Additionally, an out-of-distribution medication recommendation experiment demonstrates LAMO's remarkable accuracy even with medications outside the training data.