Align Then Adapt: Label-Efficient Adapter Learning for Asymmetric Dense Retrieval

arXiv:2604.03403 · cs.IR, cs.CL · Submitted 2026-04-03 · Read on arXiv

cs.IR, cs.CL

Submitted: 2026-04-03

Updated: 2026-08-26

Comments: EMNLP2026

Code: https://github.com/megagonlabs/ERA

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 41× in this paper · explained in 1 episode
• Alignment — 28× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• Embeddings — 15× in this paper · explained in 2 episodes
• Fine-Tuning — 11× in this paper · explained in 5 episodes
• Semantic Gap — 9× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Efficient Retrieval Adapter — 5× in this paper
• Empirical Methods in Natural Language Processing — 5× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• Representation Learning with Contrastive Predictive Coding
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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