mlsanityData to 7 Oct 2026

ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling

arXiv 2402.13542 ↗First posted 21 Feb 2024cs.CL cs.AI cs.IRcited by 10

On the map: Retrieval-augmented generation → Generation and context integration

Abstract (arXiv)

Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with LLMs due to their separate training processes and the black-box nature of LLMs. To address this challenge, we propose ARL2, a retriever learning technique that harnesses LLMs as labelers. ARL2 leverages LLMs to annotate and score relevant evidence, enabling learning the retriever from robust LLM supervision. Furthermore, ARL2 uses an adaptive self-training strategy for curating high-quality and diverse relevance data, which can effectively reduce the annotation cost. Extensive experiments demonstrate the effectiveness of ARL2, achieving accuracy improvements of 5.4% on NQ and 4.6% on MMLU compared to the state-of-the-art methods. Additionally, ARL2 exhibits robust transfer learning capabilities and strong zero-shot generalization abilities. Our code will be published at urlhttps://github.com/zhanglingxi-cs/ARL2.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation. Builds on 39 of its 58 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

39 of 58 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models15 Nov
  2. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuning2 Oct
  3. 2023Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training12 Jun
  4. 2023In-Context Retrieval-Augmented Language Models31 Jan
  5. 2023REPLUG: Retrieval-Augmented Black-Box Language Models30 Jan
  6. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Models5 Aug
  7. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks22 May

Other references · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  5. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  7. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
  8. 2017Billion-scale similarity search with GPUscited by 2,477
  9. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  10. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  11. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  12. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
20 more
  1. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,203
  2. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 846
  3. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
  4. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
  5. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
  6. 2021Large Dual Encoders Are Generalizable Retrieverscited by 454
  7. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  8. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  9. 2021Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrievalcited by 203
  10. 2021RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-rankingcited by 175
  11. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  12. 2022RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encodercited by 118
  13. 2023How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrievalcited by 116
  14. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  15. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 93
  16. 2021Adversarial Retriever-Ranker for dense text retrievalcited by 82
  17. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  18. 2018End-to-End Retrieval in Continuous Spacecited by 46
  19. 2023Unicron: Economizing Self-Healing LLM Training at Scalecited by 16
  20. 2023BERT is not The Count: Learning to Match Mathematical Statements with Proofscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 1.

10 papers.

  1. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  2. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  3. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Pathscited by 60
  4. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  5. 2025MLP Memory: A Retriever-Pretrained Memory for Large Language Modelscited by 19
  6. 2024Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generationcited by 13
  7. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7
  8. 2025DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generationcited by 1
  9. 2025Optimizing What Matters: AUC-Driven Learning for Robust Neural Retrievalcited by 0
  10. 2025DMA: Online RAG Alignment with Human Feedbackcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
  2. 2024Assessing "Implicit" Retrieval Robustness of Large Language Models
  3. 2024Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
  4. 2025LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexing
  5. 2023"Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation
  6. 2023Retrieval meets Long Context Large Language Models
  7. 2026To Memorize or to Retrieve: Scaling the Interaction Between Pretraining and Retrieval
  8. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG