RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs

arXiv:2603.08166 · cs.CL · Submitted 2026-03-09 · Read on arXiv

cs.CL

Submitted: 2026-03-09

Updated: 2026-09-23

Comments: 19 pages, 7 figures. Accepted for publication in IEEE Journal of Biomedical and Health Informatics (J-BHI)

Code: https://github.com/DUTIR-BioNLP/RexDrug

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 54× in this paper · explained in 2 episodes
• Reasoning Traces — 41× in this paper · explained in 3 episodes
• Accuracy — 18× in this paper · explained in 1 episode
• Alignment — 14× in this paper · explained in 1 episode
• Consistency — 14× in this paper · explained in 3 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Interpretability — 8× in this paper · explained in 15 episodes
• F1 Score — 7× in this paper · explained in 3 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• GRPO — 6× in this paper · explained in 6 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Multi-agent Collaboration — 5× in this paper · explained in 2 episodes
• Semantic Alignment — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Distillation — 3× in this paper · explained in 2 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Knowledge Graph — 3× in this paper · explained in 7 episodes
• Two-Stage Training Strategy — 3× in this paper · explained in 3 episodes
• Two-stage training — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Automated Drug Combination Extraction — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Named Entity Recognition (NER) — 2× in this paper · explained in 2 episodes
• Perplexity — 2× in this paper · explained in 3 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Supervised Fine-Tuning — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Error Localization — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Group Relative Policy Optimization — 1× in this paper
• Group Relative Policy Optimization (GRPO) — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Linguistic Coherence — 1× in this paper
• Logical Inference — 1× in this paper
• Reinforcement Learning (RL) — 1× in this paper · explained in 24 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• A Dataset for N-ary Relation Extraction of Drug Combinations
• Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
• InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
• N-ary Relation Extraction using Graph State LSTM
• Document-Level $N$-ary Relation Extraction with Multiscale Representation Learning
• Inspire the Large Language Model by External Knowledge on BioMedical Named Entity Recognition
• ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
• Open-Medical-R1: How to Choose Data for RLVR Training at Medicine Domain
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
• LLMs are not Zero-Shot Reasoners for Biomedical Information Extraction
• Attention-Wrapped Hierarchical BLSTMs for DDI Extraction
• ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning

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