Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation (arXiv, Feb 2025)
- Paper: Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
- Authors: Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Jimmy Lin
- Source: arXiv, February 2025
- Category: Embedding models
- The paper studies how to make general-purpose dense retrieval models specialize to a target domain, combining listwise distillation (transferring full ranking knowledge from a stronger teacher) with LLM-driven data augmentation to compensate for scarce in-domain training data.
- It is situated in the modern neural IR landscape: BM25 → cross-encoders → bi-encoder dense retrieval → late interaction → generative retrieval and LLM-based agentic search, where each generation trades off efficiency, effectiveness, and maintainability.
- Dense retrievers offer millisecond-level recall via approximate nearest neighbor search but are sensitive to domain shift and long-tail queries — the core problem this work targets.
- Common datasets: MS MARCO, BEIR, Natural Questions, domain-specific corpora.
- Common metrics: nDCG@10, MRR, Recall@k, Hit@k, plus latency and token cost.
- Typical baselines: BM25, dense retrieval, cross-encoder rerankers, retrieval-free LLMs, commercial search APIs.
- Exact quantitative results should be verified against the original PDF; this entry summarizes design logic from the abstract and public metadata.
- Experiments may be constrained by GPU budgets; benchmarks may not match real user distributions.
- English-centric data leaves cross-lingual generalization unverified.
- Open problems include trustworthy evaluation, latency/cost trade-offs, hallucination and safety, and cross-lingual/multimodal extension.
- The Scandinavian Embedding Benchmarks
- A Universal Framework for Compressing Embeddings in CTR Prediction
- Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
- BGE M3-Embedding
- BGE-en-ICL / BGE-ICL: Few-Shot Learning for Text Embedders
- Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval
- Original paper: <https://arxiv.org/abs/2502.19712>
Key points
Methodological framing
The typical pipeline follows: problem formalization → model/system design → training procedure → inference pipeline:
1. Input & representation: encode queries, documents, and user context into dense or sparse representations. 2. Core modules: retrievers, rerankers, planners, memory/tool interfaces arranged in series or parallel. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning, and synthetic data generation. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Evaluation considerations
Insights for search & recommendation systems
1. Architecture: cascaded retrieve-rerank-generate remains mainstream, but agentic paradigms make the number and strategy of retrieval steps themselves learnable. 2. Data: high-quality instruction data and click/session logs are as critical as model architecture; synthetic data must guard against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human assessment. 4. Deployment: latency, cost, explainability, and safety are hard production constraints — optimizing academic benchmarks alone is insufficient.