The DOI matches its Crossref record; 72 of the 74 references it cites were found in a registry or an index.
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Open accessAbstract
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“This winner-take-all formulation has been shown to be both effective and computationally attractive in multi-vector retrieval systems [1, 4, 5].”
From Spike Hijacking in Late-Interaction Retrieval (2026) · cites this paper as [4]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“We compare HippoRAG with existing RAG methods on multi-hop question answering (QA) and show that our method outperforms the state-of-the-art methods remarkably, by up to 20 %.”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [70]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 1×
“Product quantization (Gray, 1984; Jegou et al., 2010) compresses a single vector by splitting it into small sub-vectors and encoding each of them using an ID within a codebook.”
BackgroundMS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
DBpedia: A Nucleus for a Web of Open Data
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-76298-0_52 · Cited by 2,454
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690
FEVER: a Large-scale Dataset for Fact Extraction and VERification
Thorne, J., Vlachos, A., Christodoulopoulos, C., & Mittal, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1074 · Cited by 1,343
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301
Self-citationLatent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Cohan, A., Feldman, S., Beltagy, I., Downey, D., & Weld, D. S. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.207 · Cited by 769
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., & Zhou, M. (2020) · arXiv · doi:10.48550/arxiv.2002.10957 · Cited by 703
Nogueira, R., & Cho, K. (2019) · arXiv · doi:10.48550/arxiv.1901.04085 · Cited by 615
Qu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W. X., Dong, D., Wu, H. R., & Wang, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.466 · Cited by 597
REALM: Retrieval-Augmented Language Model Pre-Training
Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020) · arXiv · doi:10.48550/arxiv.2002.08909 · Cited by 574
SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking
Formal, T., Piwowarski, B., & Clinchant, S. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463098 · Cited by 526
Fact or Fiction: Verifying Scientific Claims
Wadden, D., Lin, S., Lo, K., Wang, L. L., van Zuylen, M., Cohan, A., & Hajishirzi, H. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.609 · Cited by 382
Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04426 · Cited by 382
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P. N., Ahmed, J., & Overwikj, A. (2021) · International Conference on Learning Representations · Cited by 376
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Hofstätter, S., Lin, S.-C., Yang, J.-H., Lin, J., & Hanbury, A. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462891 · Cited by 374
Sparse, Dense, and Attentional Representations for Text Retrieval
Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00369 · Cited by 363
SPLADE v2: Sparse Lexical and Expansion Model for Information RetrievalPreprint
Formal, T., Lassance, C., Piwowarski, B., & Clinchant, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.10086 · Cited by 297
A Full-Text Learning to Rank Dataset for Medical Information Retrieval
Boteva, V., Gholipour, D., Sokolov, A., & Riezler, S. (2016) · European Conference on Information Retrieval · doi:10.1007/978-3-319-30671-1_58 · Cited by 288
CLIMATE-FEVER: A Dataset for Verification of Real-World Climate ClaimsPreprint
Diggelmann, T., Boyd-Graber, J. L., Bulian, J., Ciaramita, M., & Leippold, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00614 · Cited by 280
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
Gao, L., Dai, Z., & Callan, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.241 · Cited by 265
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · arXiv · doi:10.48550/arxiv.2104.08051 · Cited by 251
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Gao, L., & Callan, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.203 · Cited by 251
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462880 · Cited by 248
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K.-F., Liu, J., Bennett, P., Ahmed, J., & Overwijk, A. (2020) · arXiv · doi:10.48550/arxiv.2007.00808 · Cited by 204
Humeau, S., Shuster, K., Lachaux, M.-A., & Weston, J. (2020) · International Conference on Learning Representations · Cited by 177
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
Ren, R., Qu, Y., Liu, J., Zhao, W. X., She, Q., Wu, H. R., Wang, H., & Wen, J.-R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.224 · Cited by 177
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Chen, C., Choi, J., Brand, D., Agrawal, A., Zhang, W., & Gopalakrishnan, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11728 · Cited by 176
Learning Passage Impacts for Inverted Indexes
Mallia, A., Khattab, O., Suel, T., & Tonellotto, N. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3463030 · Cited by 174
Self-citationHoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification
Jiang, Y., Bordia, S., Zhong, Z., Dognin, C., Singh, M., & Bansal, M. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.309 · Cited by 173
Lin, J., & Ma, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14807 · Cited by 154
RepBERT: Contextualized Text Embeddings for First-Stage RetrievalPreprint
Zhan, J., Mao, J., Liu, Y., Zhang, M., & Ma, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15498 · Cited by 152
Context-Aware Term Weighting For First Stage Passage Retrieval
Dai, Z., & Callan, J. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401204 · Cited by 150
Distilling Dense Representations for Ranking using Tightly-Coupled TeachersPreprint
Lin, S.-C., Yang, J.-H., & Lin, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11386 · Cited by 141
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
MacAvaney, S., Nardini, F. M., Perego, R., Tonellotto, N., Goharian, N., & Frieder, O. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401093 · Cited by 130
Advances in residual vector quantization: a review
Barnes, C. F., Rizvi, S. A., & Nasrabadi, N. M. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.480761 · Cited by 108
Retrieval of the Best Counterargument without Prior Topic Knowledge
Wachsmuth, H., Syed, S., & Stein, B. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1023 · Cited by 106
Learning Dense Representations of Phrases at Scale
Lee, J., Sung, M., Kang, J., & Chen, D. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.518 · Cited by 105
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482358 · Cited by 91
Efficient Passage Retrieval with Hashing for Open-domain Question Answering
Yamada, I., Asai, A., & Hajishirzi, H. (2021) · doi:10.18653/v1/2021.acl-short.123 · Cited by 88
Relevance-guided Supervision for OpenQA with ColBERT
Khattab, O., Potts, C., & Zaharia, M. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00405 · Cited by 81
Self-citationPAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Ren, R., Lv, S., Qu, Y., Liu, J., Zhao, W. X., She, Q., Wu, H., Wang, H., & Wen, J.-R. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.191 · Cited by 76
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2022) · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3488560.3498443 · Cited by 72
TREC-COVID: Constructing a Pandemic Information Retrieval Test CollectionPreprint
Voorhees, E. M., Alam, T., Bedrick, S. D., Demner‐Fushman, D., Hersh, W., Lo, K., Roberts, K. E., Soboroff, I. M., & Wang, L. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.04474 · Cited by 68
Qu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W. X., Dong, D., Wu, H. R., & Wang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08191 · Cited by 66
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge DistillationPreprint
Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M., & Hanbury, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02666 · Cited by 63
Modularized Transfomer-based Ranking Framework
Gao, L., Dai, Z., & Callan, J. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.342 · Cited by 54
GooAQ: Open Question Answering with Diverse Answer Types
Khashabi, D., Ng, A., Khot, T., Sabharwal, A., Hajishirzi, H., & Callison-Burch, C. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.38 · Cited by 50
Domain-matched Pre-training Tasks for Dense Retrieval
Oğuz, B., Lakhotia, K., Gupta, A., Lewis, P., Karpukhin, V., Piktus, A., Chen, X., Riedel, S., Yih, S., Gupta, S., et al. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.114 · Cited by 50
A Memory Efficient Baseline for Open Domain Question AnsweringPreprint
Izacard, G., Petroni, F., Hosseini, L., Cao, N. D., Riedel, S., & Grave, É. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15156 · Cited by 47
Projected Residual Vector Quantization for ANN Search
Wei, B., Guan, T., & Yu, J. (2014) · IEEE Multimedia · doi:10.1109/mmul.2013.65 · Cited by 44
Phrase Retrieval Learns Passage Retrieval, Too
Lee, J., Wettig, A., & Chen, D. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.297 · Cited by 41
SPLADE: Sparse Lexical and Expansion Model for First Stage RankingPreprint
Formal, T., Piwowarski, B., & Clinchant, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05720 · Cited by 38
On Approximate Nearest Neighbour Selection for Multi-Stage Dense Retrieval
Macdonald, C., & Tonellotto, N. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482156 · Cited by 37
A Double Residual Compression Algorithm for Efficient Distributed LearningPreprint
Liu, X., Li, Y., Tang, J., & Yan, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.07561 · Cited by 36
TRQ: Ternary Neural Networks With Residual Quantization
Li, Y., Ding, W., Liu, C., Zhang, B., & Guo, G. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17036 · Cited by 36
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Xin, J., Xiong, C., Srinivasan, A., Sharma, A., Jose, D., & Bennett, P. N. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.316 · Cited by 33
Hindsight: Posterior-guided training of retrievers for improved open-ended generationPreprint
Paranjape, A., Khattab, O., Potts, C., Zaharia, M., & Manning, C. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07752 · Cited by 30
Self-citationLearning To Retrieve: How to Train a Dense Retrieval Model Effectively and EfficientlyPreprint
Zhan, J., Mao, J., Liu, Y., Zhang, M., & Ma, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10469 · Cited by 30
Optimized residual vector quantization for efficient approximate nearest neighbor search
Ai, L., Yu, J., Wu, Z., He, Y., & Guan, T. (2015) · Multimedia Systems · doi:10.1007/s00530-015-0470-9 · Cited by 29
Residual Quantization for Low Bit-Width Neural Networks
Li, Z., Ni, B., Yang, X., Zhang, W., & Gao, W. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3124095 · Cited by 23
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed RetrievalPreprint
Khattab, O., Potts, C., & Zaharia, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00436 · Cited by 22
Self-citationSDR: Efficient Neural Re-ranking using Succinct Document Representation
Cohen, N., Portnoy, A., Fetahu, B., & Ingber, A. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.457 · Cited by 13
Multi-Vector Attention Models for Deep Re-ranking
Zhou, G., & Devlin, J. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.443 · Cited by 12
Research works printed but not linked: 7
- Alexander Bondarenko, Maik Fröbe, Meriem Be- loucif, Lukas Gienapp, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, et al. 2020. Overview of touché 2020: Argument Retrieval. In Interna- tional Conference of the Cross-Language Evalua- tion Forum for European Languages, pages 384- 395. Springer.
- Robert Gray. 1984. Vector quantization. IEEE Assp Magazine, 1(2):4-29.
- PMLR.
- Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018. WWW'18 Open Chal- lenge: Financial Opinion Mining and Question An- swering. In Companion Proceedings of the The Web Conference 2018, pages 1941-1942.
- Nandan Thakur, Nils Reimers, Andreas Rücklé, Ab- hishek Srivastava, and Iryna Gurevych. 2021. BEIR: A Heterogenous Benchmark for Zero-shot Eval- uation of Information Retrieval Models. arXiv preprint arXiv:2104.08663. · arXiv
- Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021b. Efficient passage retrieval with hashing for open-domain question answering. In Proceedings of the 59th Annual Meeting of the Association for Com- putational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 979-986, Online. Association for Computational Linguistics. · DOI
- Peilin Yang, Hui Fang, and Jimmy Lin. 2018a. Anserini: Reproducible ranking baselines using lucene. Journal of Data and Information Quality (JDIQ), 10(4):1-20.
Cited by
Sorted by citations
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211
(2022) · doi:10.18653/v1/2022.emnlp-main.669 · Cited by 177
Retrieval-augmented generation for educational application: A systematic survey
Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., & Wang, F. L. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100417 · Cited by 151
From Distillation to Hard Negative Sampling
Formal, T., Lassance, C., Piwowarski, B., & Clinchant, S. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531857 · Cited by 191
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
Saad-Falcon, J., Khattab, O., Potts, C., & Zaharia, M. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.20 · Cited by 148
Self-citation
Metadata
- Authors
- Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 3715-3734
- OpenAlex
- W3217305727
- Cited by
- 353
- References
- 74
- Citations by year
- 2026: 123 · 2025: 99 · 2024: 49 · 2023: 64 · 2022: 18
- FWCI
- 51.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1651570, CAREER)
- Cisco Systems
- VMware
- Ant Financial Services Group
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- Colbert, efficient retrieval, dense retrieval, word embeddings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
