"Multi-task retriever fine-tuning for domain-specific and efficient RAG"

One small retriever does all the heavy lifting for domain RAG applications.

A single retriever model fine-tuned on multiple domain tasks enables efficient, scalable RAG applications while maintaining multilingual capabilities and strong generalization across domains.

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https://arxiv.org/abs/2501.04652

🤔 Original Problem:

Deploying RAG applications faces two key challenges: retrieving domain-specific information and managing multiple retrievers for different applications is computationally expensive and inefficient.

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🔧 Solution in this Paper:

→ The paper instruction fine-tunes a small retriever encoder (mGTE-base, 305M parameters) on various domain tasks

→ The training combines workflow steps, database tables, and field retrieval tasks into a single model

→ Data balancing through frequency-based downsampling prevents bias from overrepresented steps

→ The multi-task approach enables one retriever to serve multiple applications efficiently

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💡 Key Insights:

→ Multi-task instruction fine-tuning improves generalization across domains

→ Downsampling frequent components boosts step retrieval performance by 8%

→ The model preserves multilingual capabilities despite English-only training

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📊 Results:

→ Step retrieval: 0.90 recall@15 on out-of-domain tests

→ Table retrieval: 0.90 recall@5 across domains

→ Field retrieval: 0.60 recall@5 for database fields

→ Workflow retrieval: 0.94 recall@5 on unseen tasks