RAG Context Reranking
Re-rank candidate passages retrieved from a VectorDB (initial recall via embeddings), improving final context selection for downstream medical LLM reasoning.
EMR Profile Reranking
Re-rank patient historical information (e.g., past assessments, diagnoses, medications) to surface the most clinically relevant records for a given current assessment.

The reranker outputs a direct relevance score for each (query, passage) pair and can be used as a drop-in “second-stage” ranking component after embedding-based retrieval.

Why a Reranker?

Embedding retrieval is fast and scalable but may miss nuanced relevance (clinical relationships, subtle terminology, long context dependencies).
A reranker improves precision by explicitly scoring each candidate passage against the query, typically yielding better top-k context for medical QA and decision support.

Base Model

Model: BAAI/bge-reranker-v2-gemma
Finetuning strategy: LoRA (parameter-efficient fine-tuning) with gradient checkpointing and mixed precision (fp16/bf16 depending on GPU).
Rationale: Gemma-based rerankers generally provide strong relevance modeling and support longer contexts compared to smaller rerankers.

Training Data

1) BioASQ (Generated Queries)

Used as: (query, document) positives; negatives sampled from rolling buffer.
Specialised to handle the complex terminology and high precision required for Task B (Biomedical Semantic QA). The reranker acts as a critical second stage in a two-stage retrieval system, filtering initial candidate lists from a PubMed-indexed retriever to ensure the highest-ranked documents contain the specific evidence needed for factoid and 'ideal' answer generation.

2) MIRIAD (Medical IR Instruction Dataset)

Used as: (question → passage) positives; negatives sampled from rolling buffer.
MIRIAD's 4.4M literature-grounded QA pairs, the model is trained to distinguish between highly similar clinical concepts. This specialization reduces medical hallucinations and ensures that the most scientifically accurate evidence is prioritised in a multi-stage retrieval pipeline for healthcare professionals.

3) SciDocs

Multi-task dataset—including citation prediction and co-citation analysis—the model learns to capture nuanced semantic relationships that standard Bi-Encoders miss. The resulting reranker serves as a high-accuracy second stage in a two-stage retrieval pipeline, significantly improving Top-K relevance for complex scholarly queries.

Methodology

Data Construction (Triplets)

The training corpus is converted into reranker triplets:

json

{
  "query": "clinical question",
  "pos": ["relevant passage 1", "relevant passage 2"],
  "neg": ["irrelevant passage A", "irrelevant passage B"],
  "source": "dataset_name"
}

Positives: from dataset relevance labels or paired question–passage examples.
Negatives: sampled from an in-memory rolling buffer (fast, scalable offline).
Output splits: train / val / test created in one run.

Evaluation

Computes IR ranking metrics by scoring each query against its (pos + neg) candidates:

nDCG@10: 0.60+
MRR@10: 0.50+
MAP@10: 0.40+
Hit@1: 0.40+
Metrics reported overall and broken down by data source.

MedSwin/MedSwin-Reranker-bge-gemma

Fine-tuned Biomedical & EMR Context Ranking

Overview