Principle:FlagOpen FlagEmbedding Reinforced Domain Adaptation
| Knowledge Sources | |
|---|---|
| Domains | Machine Learning, Information Retrieval, Domain Adaptation, Reinforcement Learning |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Reinforced iterative retrieval domain adaptation that uses LLM feedback to progressively generate high-quality training data and improve retriever performance on target domains through multi-round optimization.
Description
This principle tackles the challenge of adapting general-purpose retrievers to specific domains with limited labeled data. The approach implements an iterative pipeline where a retriever model and an LLM-based data generator mutually improve each other. Starting with a small seed corpus, the system uses the current retriever to find relevant documents, prompts an LLM to generate queries for those documents, evaluates generation quality through LLM-as-judge feedback, and uses the curated data to retrain the retriever. Multiple rounds of this cycle progressively improve both the retriever's domain-specific performance and the generator's ability to create realistic queries. The method also incorporates distillation techniques to compress knowledge from stronger models and universal query generation to enhance diversity.
Usage
Use this principle when:
- Adapting retrieval systems to specialized domains (legal, medical, technical)
- Building domain-specific search with limited annotated data
- Improving retrieval quality through iterative refinement
- Leveraging LLM feedback for data quality control
Theoretical Basis
The reinforced adaptation pipeline follows these iterative steps:
- Initial Retrieval: Use current retriever R_t to find relevant docs: D = R_t(corpus, query_seeds)
- LLM-based Generation: Generate queries for retrieved documents: Q = LLM("Generate query for: " + d) for d ∈ D
- Quality Assessment:
- LLM evaluates query quality: score = LLM("Rate query relevance: " + q + " for doc: " + d)
- Filter low-quality pairs: D_train = {(q, d) | score > threshold}
- Retriever Training: Update retriever with new data: R_{t+1} = train(R_t, D_train, L_contrastive)
- Distillation: Optionally distill from strong teacher: L = L_retrieval + α*KL(student || teacher)
- Universal Query Expansion: Generate diverse query types (factual, analytical, comparative) to improve coverage
Convergence: Iterate until retrieval metrics plateau or max iterations reached
The reinforcement signal comes from LLM feedback guiding data generation toward higher quality, while the retriever's improvement enables better document selection in subsequent rounds.
Related Pages
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Get_Prompts
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Data_Utils
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Retriever_Dataset
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_GPTAgent
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Generate_Generator_Data
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Generate_Retriever_Data
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Generate_Distill_Data
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Generate_Universal_Query
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Retriever_Modeling
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Model
- Implementation:FlagOpen_FlagEmbedding_Reinforced_IR_Multi_GPU