Modular Expert Merging for Biomedical Retrieval

arXiv:2602.04731 · cs.CL, cs.LG · Submitted 2026-02-04 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-02-04

Updated: 2026-09-02

Terminology

• Fine-Tuning — 27× in this paper · explained in 5 episodes
• Model Merging — 14× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• RAG — 11× in this paper · explained in 3 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Contrastive Learning — 5× in this paper · explained in 5 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Grounding — 3× in this paper · explained in 3 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Retrieval-Augmented Generation (RAG) — 3× in this paper · explained in 16 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
• Phi-4 Technical Report
• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• Efficient Natural Language Response Suggestion for Smart Reply
• MMTEB: Massive Multilingual Text Embedding Benchmark
• Mistral 7B
• DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
• MTEB: Massive Text Embedding Benchmark
• Large Dual Encoders Are Generalizable Retrievers
• SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
• Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
• BM25S: Orders of magnitude faster lexical search via eager sparse scoring
• ReasonIR: Training Retrievers for Reasoning Tasks
• NV-Retriever: Improving text embedding models with effective hard-negative mining
• Gemma: Open Models Based on Gemini Research and Technology
• EmbeddingGemma: Powerful and Lightweight Text Representations
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Qwen3 Technical Report
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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