mlsanityData to 14 Oct 2026

ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval

arXiv 2501.15245 ↗First posted 25 Jan 2025cs.CLcited by 9

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) models have drawn considerable attention in modern open-domain question answering. The effectiveness of RAG depends on the quality of the top retrieved documents. However, conventional retrieval methods sometimes fail to rank the most relevant documents at the top. In this paper, we introduce ASRank, a new re-ranking method based on scoring retrieved documents using zero-shot answer scent which relies on a pre-trained large language model to compute the likelihood of the document-derived answers aligning with the answer scent. Our approach demonstrates marked improvements across several datasets, including NQ, TriviaQA, WebQA, ArchivalQA, HotpotQA, and Entity Questions. Notably, ASRank increases Top-1 retrieval accuracy on NQ from 19.2% to 46.5% for MSS and 22.1% to 47.3% for BM25. It also shows strong retrieval performance on several datasets compared to state-of-the-art methods (47.3 Top-1 by ASRank vs 35.4 by UPR by BM25).

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 40 of its 60 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

40 of 60 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering9 Jun

Other references · 39

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2023Qwen Technical Reportcited by 3,344
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  10. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  11. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  12. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
27 more
  1. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  2. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  3. 2022A Survey on In-context Learningcited by 721
  4. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  6. 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 413
  7. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  8. 2020Overview of the TREC 2019 deep learning trackcited by 286
  9. 2021SPLADE: Sparse Lexical and Expansion Model for First Stage Rankingcited by 286
  10. 2023Fine-Tuning LLaMA for Multi-Stage Text Retrievalcited by 272
  11. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  12. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 235
  13. 2020Distilling Knowledge from Reader to Retriever for Question Answeringcited by 226
  14. 2022SGPT: GPT Sentence Embeddings for Semantic Searchcited by 204
  15. 2022Improving Passage Retrieval with Zero-Shot Question Generationcited by 165
  16. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 162
  17. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 149
  18. 2019Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answeringcited by 147
  19. 2021Simple Entity-Centric Questions Challenge Dense Retrieverscited by 140
  20. 2021End-to-End Training of Neural Retrievers for Open-Domain Question Answeringcited by 75
  21. 2023InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrievalcited by 73
  22. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documentscited by 49
  23. 2020Differentiable Reasoning over a Virtual Knowledge Basecited by 39
  24. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  25. 2024ArabicaQA: A Comprehensive Dataset for Arabic Question Answeringcited by 20
  26. 2022Few-shot Reranking for Multi-hop QA via Language Model Promptingcited by 19
  27. 2024Exploring Hint Generation Approaches in Open-Domain Question Answeringcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 2.

9 papers.

  1. 2025Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generationcited by 13
  2. 2025DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillationcited by 11
  3. 2025RankArena: A Unified Platform for Evaluating Retrieval, Reranking and RAG with Human and LLM Feedbackcited by 5
  4. 2025A Survey of LLM × DATAcited by 4
  5. 2025How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Modelscited by 4
  6. 2025Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankerscited by 3
  7. 2025From Retrieval to Generation: Comparing Different Approachescited by 2
  8. 2026BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Eliminationcited by 0
  9. 2026Test-Time Training for Zero-Resource Dense Retrieval Rerankingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025InfoGain-RAG: Boosting Retrieval-Augmented Generation via Document Information Gain-based Reranking and Filtering
  2. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  3. 2024Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
  4. 2025Question Decomposition for Retrieval-Augmented Generation
  5. 2026SciRet: A Compute-Aware Empirical Study of Retrieval and Reranking for Scientific RAG
  6. 2026Re-ranking and Late Interaction Drive Retrieval Quality: A Controlled Comparison of RAG Strategies for Scientific Question Answering
  7. 2024ARAGOG: Advanced RAG Output Grading
  8. 2026Beyond the Reranker: Do RAG Retrieval Enhancements Help Once a Strong Reranker Is Present?