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Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that store knowledge.
1907
Earlier work this paper cites.
1910
Earlier work this paper cites.
Ishiwatari S, Yao J, Liu S, et al (2017) Chunk-based decoder for neural machine translation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1901–1912
1912
Earlier work this paper cites.
Uddin MN, Saeidi A, Handa D, et al (2025) Unseentimeqa: Time-sensitive question-answering beyond llms’ memorization. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 1873–1913
1913
Earlier work this paper cites.
Harris ZS (1954) Distributional structure. Word 10(2-3):146–162
1954
Earlier work this paper cites.
2001
Earlier work this paper cites.
NLTK (2001) NLTK. misc, URL https://www.nltk.org/
2001
Earlier work this paper cites.
Gunawardana A, Shani G (2009) A survey of accuracy evaluation metrics of recommendation tasks. Journal of Machine Learning Research 10:2935–2962
2009
Earlier work this paper cites.
Robertson SE, Zaragoza H (2009) The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval 3(4):333–389
2009
Earlier work this paper cites.
Harris D, Harris S (2010) Digital design and computer architecture. Morgan Kaufmann
2010
Earlier work this paper cites.
Jégou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1):117–128
2011
Earlier work this paper cites.
Rajaraman A, Ullman JD (2011) Data Mining, Cambridge University Press, p 1–17
2011
Earlier work this paper cites.
Facebook (2013) RocksDB. Software, URL https://github.com/facebook/rocksdb
2013
Earlier work this paper cites.
LMDB (2014) LMDB. Software, URL https://github.com/LMDB/lmdb
2014
Earlier work this paper cites.
Liu S, Wei Y (2015) Fast nearest neighbor searching based on improved vp-tree. Pattern Recognition Letters 60-61:8–15
2015
Earlier work this paper cites.
explosion (2016) Spacy. misc, URL https://spacy.io/
2016
Earlier work this paper cites.
Muszynska E (2016) Graph- and surface-level sentence chunking. In: Proceedings of the ACL 2016 Student Research Workshop, pp 93–99
2016
Earlier work this paper cites.
Spotify (2017) Annoy. Software, URL https://github.com/spotify/annoy
2017
Earlier work this paper cites.
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in Neural Information Processing Systems 30 (NeurIPS), pp 5998–6008
2017
Earlier work this paper cites.
Mihaylov T, Clark P, Khot T, et al (2018) Can a suit of armor conduct electricity? A new dataset for open book question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2381–2391
2018
Earlier work this paper cites.
Narayan S, Cohen SB, Lapata M (2018) Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1797–1807
2018
Earlier work this paper cites.
Radford A, Narasimhan K, Salimans T, et al (2018) Improving language understanding by generative pre-training. OpenAI blog
2018
Earlier work this paper cites.
Rajpurkar P, Jia R, Liang P (2018) Know what you don’t know: Unanswerable questions for squad. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), pp 784–789
2018
Earlier work this paper cites.
Wu M, Goodman ND (2018) Multimodal generative models for scalable weakly-supervised learning. In: Advances in Neural Information Processing Systems 31 (NeurIPS), pp 5580–5590
2018
Earlier work this paper cites.
Yang Z, Qi P, Zhang S, et al (2018) Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 2369–2380
2018
Earlier work this paper cites.
Devlin J, Chang M, Lee K, et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp 4171–4186
2019
Earlier work this paper cites.
Guo D, Tang D, Duan N, et al (2019) Coupling retrieval and meta-learning for context-dependent semantic parsing. In: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL), pp 855–866
2019
Earlier work this paper cites.
Kwiatkowski T, Palomaki J, Redfield O, et al (2019) Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics 7:452–466
2019
Earlier work this paper cites.
Petroni F, Rocktäschel T, Riedel S, et al (2019) Language models as knowledge bases? In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 2463–2473
2019
Earlier work this paper cites.
Radford A, Wu J, Child R, et al (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9
2019
Earlier work this paper cites.
Ram P, Sinha K (2019) Revisiting kd-tree for nearest neighbor search. In: Teredesai A, Kumar V, Li Y, et al (eds) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), pp 1378–1388
2019
Earlier work this paper cites.
Reimers N, Gurevych I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3980–3990
2019
Earlier work this paper cites.
Zhang B, Sennrich R (2019) Root mean square layer normalization. In: Advances in Neural Information Processing Systems 32 (NeurIPS), pp 12360–12371
2019
Earlier work this paper cites.
Brown TB, Mann B, Ryder N, et al (2020) Language models are few-shot learners. In: Advances in Neural Information Processing Systems 33 (NeurIPS)
2020
Earlier work this paper cites.
Castelli V, Chakravarti R, Dana S, et al (2020) The techqa dataset. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1269–1278
2020
Earlier work this paper cites.
Chalkidis I, Fergadiotis M, Malakasiotis P, et al (2020) LEGAL-BERT: the muppets straight out of law school. In: Findings of the Association for Computational Linguistics: EMNLP, pp 2898–2904
2020
Earlier work this paper cites.
Clark K, Luong M, Le QV, et al (2020) ELECTRA: pre-training text encoders as discriminators rather than generators. In: Proceedings of the 8th International Conference on Learning Representations (ICLR). OpenReview.net
2020
Earlier work this paper cites.
Fabbri AR, Ng P, Wang Z, et al (2020) Template-based question generation from retrieved sentences for improved unsupervised question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 4508–4513
2020
Earlier work this paper cites.
Févry T, Soares LB, FitzGerald N, et al (2020) Entities as experts: Sparse memory access with entity supervision. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4937–4951
2020
Earlier work this paper cites.
Gong H, Shen Y, Yu D, et al (2020) Recurrent chunking mechanisms for long-text machine reading comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 6751–6761
2020
Earlier work this paper cites.
Guo R, Sun P, Lindgren E, et al (2020) Accelerating large-scale inference with anisotropic vector quantization. In: Proceedings of the 37th International Conference on Machine Learning (ICML), pp 3887–3896
2020
Earlier work this paper cites.
Guu K, Lee K, Tung Z, et al (2020) Retrieval augmented language model pre-training. In: Proceedings of the 37th International Conference on Machine Learning (ICML), pp 3929–3938
2020
Earlier work this paper cites.
Holtzman A, Buys J, Du L, et al (2020) The curious case of neural text degeneration. In: Proceedings of the 8th International Conference on Learning Representations (ICLR)
2020
Earlier work this paper cites.
Hossain N, Ghazvininejad M, Zettlemoyer L (2020) Simple and effective retrieve-edit-rerank text generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pp 2532–2538, 10.18653/V1/2020.ACL-MAIN.228
2020
Earlier work this paper cites.
Jiao X, Yin Y, Shang L, et al (2020) Tinybert: Distilling BERT for natural language understanding. In: Findings of the Association for Computational Linguistics: EMNLP, pp 4163–4174
2020
Earlier work this paper cites.
Karpukhin V, Oguz B, Min S, et al (2020) Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 6769–6781
2020
Earlier work this paper cites.
Khandelwal U, Levy O, Jurafsky D, et al (2020) Generalization through memorization: Nearest neighbor language models. In: Proceedings of The 8th International Conference on Learning Representations (ICLR)
2020
Earlier work this paper cites.
Lewis PSH, Perez E, Piktus A, et al (2020) Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems 33 (NeurIPS)
2020
Earlier work this paper cites.
Malkov YA, Yashunin DA (2020) Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 42(4):824–836
2020
Earlier work this paper cites.
Sellam T, Das D, Parikh AP (2020) BLEURT: learning robust metrics for text generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics, pp 7881–7892
2020
Earlier work this paper cites.
Bai H, Zhang W, Hou L, et al (2021) Binarybert: Pushing the limit of BERT quantization. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 4334–4348
2021
Earlier work this paper cites.
Cai D, Wang Y, Li H, et al (2021) Neural machine translation with monolingual translation memory. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 7307–7318
2021
Earlier work this paper cites.
Chen W, Wang X, Wang WY (2021) A dataset for answering time-sensitive questions. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks)
2021
Earlier work this paper cites.
Fabbri AR, Kryscinski W, McCann B, et al (2021) Summeval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics 9:391–409
2021
Earlier work this paper cites.
Fan A, Gardent C, Braud C, et al (2021) Augmenting transformers with knn-based composite memory for dialog. Transactions of the Association for Computational Linguistics 9:82–99
2021
Earlier work this paper cites.
Ganesh P, Chen Y, Lou X, et al (2021) Compressing large-scale transformer-based models: A case study on BERT. Transactions of the Association for Computational Linguistics 9:1061–1080
2021
Earlier work this paper cites.
Gao T, Yao X, Chen D (2021) Simcse: Simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 6894–6910
2021
Earlier work this paper cites.
Izacard G, Grave E (2021) Leveraging passage retrieval with generative models for open domain question answering. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp 874–880
2021
Earlier work this paper cites.
Johnson J, Douze M, Jégou H (2021) Billion-scale similarity search with gpus. IEEE Transactions on Big Data 7(3):535–547
2021
Earlier work this paper cites.
Khandelwal U, Fan A, Jurafsky D, et al (2021) Nearest neighbor machine translation. In: Proceedings of the 9th International Conference on Learning Representations (ICLR)
2021
Earlier work this paper cites.
Kim S, Gholami A, Yao Z, et al (2021) I-BERT: integer-only BERT quantization. In: Proceedings of the 38th International Conference on Machine Learning (ICML), pp 5506–5518
2021
Earlier work this paper cites.
Li Y, Yang Y, Quan X, et al (2021) Retrieve & memorize: Dialog policy learning with multi-action memory. In: Findings of the Association for Computational Linguistics (ACL/IJCNLP), pp 447–459
2021
Earlier work this paper cites.
Sachan DS, Reddy S, Hamilton WL, et al (2021) End-to-end training of multi-document reader and retriever for open-domain question answering. In: Advances in Neural Information Processing Systems 34 (NeurIPS), pp 25968–25981
2021
Earlier work this paper cites.
Shuster K, Poff S, Chen M, et al (2021) Retrieval augmentation reduces hallucination in conversation. In: Findings of the Association for Computational Linguistics: EMNLP, pp 3784–3803
2021
Earlier work this paper cites.
Sulem E, Hay J, Roth D (2021) Do we know what we don’t know? studying unanswerable questions beyond squad 2.0. In: Findings of the Association for Computational Linguistics: EMNLP, pp 4543–4548
2021
Earlier work this paper cites.
Thakur N, Reimers N, Rücklé A, et al (2021) BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In: Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS Datasets and Benchmarks)
2021
Earlier work this paper cites.
Xin J, Tang R, Yu Y, et al (2021) The art of abstention: Selective prediction and error regularization for natural language processing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 1040–1051
2021
Earlier work this paper cites.
Zheng X, Zhang Z, Guo J, et al (2021) Adaptive nearest neighbor machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL/IJCNLP), pp 368–374, 10.18653/V1/2021.ACL-SHORT.47
2021
Earlier work this paper cites.
Abnar S, Dehghani M, Neyshabur B, et al (2022) Exploring the limits of large scale pre-training. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR)
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Borgeaud S, Mensch A, Hoffmann J, et al (2022) Improving language models by retrieving from trillions of tokens. In: Proceedings of the 39th International Conference on Machine Learning (ICML), pp 2206–2240
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Guo R, Luan X, Xiang L, et al (2022) Manu: A cloud native vector database management system. Proceedings of the VLDB Endowment 15(12):3548–3561
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
Hu EJ, Shen Y, Wallis P, et al (2022) Lora: Low-rank adaptation of large language models. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR)
2022
Earlier work this paper cites.
Izacard G, Caron M, Hosseini L, et al (2022) Unsupervised dense information retrieval with contrastive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022
2022
Earlier work this paper cites.
Jiang H, Lu Z, Meng F, et al (2022) Towards robust k-nearest-neighbor machine translation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 5468–5477
2022
Earlier work this paper cites.
de Jong M, Zemlyanskiy Y, FitzGerald N, et al (2022) Mention memory: incorporating textual knowledge into transformers through entity mention attention. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Lewis PSH, Oguz B, Xiong W, et al (2022) Boosted dense retriever. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 3102–3117
2022
Cited alongside, same era.
Lin BY, Tan K, Miller C, et al (2022) Unsupervised cross-task generalization via retrieval augmentation. In: Advances in Neural Information Processing Systems 35 (NeurIPS)
2022
Cited alongside, same era.
Fu Y, Chen C, Chen X, et al (2024) Optimizing the number of clusters for billion-scale quantization-based nearest neighbor search. IEEE Transactions on Knowledge and Data Engineering 36(11):6786–6800
2024
Closest in time.
He Q, Wang Y, Wang W (2024) Can language models act as knowledge bases at scale? CoRR abs/2402.14273
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
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alphaXiv is searching for related work…
Liu J (2022) LlamaIndex. 10.5281/zenodo.1234 , URL https://github.com/jerryjliu/llama_index
2022
Cited alongside, same era.
Martins PH, Marinho Z, Martins AFT (2022) Chunk-based nearest neighbor machine translation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4228–4245
2022
Cited alongside, same era.
OpenAI (2022) Text-Emb-Ada. URL https://platform.openai.com/docs/guides/embeddings
2022
Cited alongside, same era.
Paranjape B, Lamm M, Tenney I (2022) Retrieval-guided counterfactual generation for QA. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp 1670–1686
2022
Cited alongside, same era.
Vaithilingam P, Zhang T, Glassman EL (2022) Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In: Barbosa SDJ, Lampe C, Appert C, et al (eds) CHI Conference on Human Factors in Computing Systems (CHI), pp 332:1–332:7
2022
Cited alongside, same era.
Wang S, Xu Y, Fang Y, et al (2022) Training data is more valuable than you think: A simple and effective method by retrieving from training data. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), pp 3170–3179
2022
Cited alongside, same era.
Wu Y, Rabe MN, Hutchins D, et al (2022) Memorizing transformers. In: Proceedings of The Tenth International Conference on Learning Representations (ICLR)
2022
Cited alongside, same era.
Zhong Z, Lei T, Chen D (2022) Training language models with memory augmentation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 5657–5673, 10.18653/V1/2022.EMNLP-MAIN.382
2022
Cited alongside, same era.
2024
Closest in time.
Hui Y, Lu Y, Zhang H (2024) UDA: A benchmark suite for retrieval augmented generation in real-world document analysis. In: Advances in Neural Information Processing Systems 38 (NeurIPS)
2024
Closest in time.
Jin J, Wang H, Zhang H, et al (2024b) DVD: dynamic contrastive decoding for knowledge amplification in multi-document question answering. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 4624–4637
2024
Closest in time.
Kim K, Lee J (2024) RE-RAG: improving open-domain QA performance and interpretability with relevance estimator in retrieval-augmented generation. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 22149–22161, 10.18653/V1/2024.EMNLP-MAIN.1236
2024
Closest in time.
Lee S, Shakir A, Koenig D, et al (2024) Open source strikes bread - new fluffy embeddings model. URL https://www.mixedbread.ai/blog/mxbai-embed-large-v1
2024
Closest in time.
Liu NF, Lin K, Hewitt J, et al (2024) Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics 12:157–173
2024
Closest in time.
Mao S, Jiang Y, Chen B, et al (2024) Rafe: Ranking feedback improves query rewriting for RAG. In: Findings of the Association for Computational Linguistics: EMNLP, pp 884–901, 10.18653/V1/2024.FINDINGS-EMNLP.49
2024
Closest in time.
Mei K, Li Z, Xu S, et al (2024) AIOS: LLM agent operating system. CoRR abs/2403.16971
2024
Closest in time.
2024
Closest in time.
Park S, Lee J (2024) Toward robust ralms: Revealing the impact of imperfect retrieval on retrieval-augmented language models. Transactions of the Association for Computational Linguistics 12:1686–1702
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Roy N, Ribeiro LFR, Blloshmi R, et al (2024) Learning when to retrieve, what to rewrite, and how to respond in conversational QA. In: Findings of the Association for Computational Linguistics: EMNLP, pp 10604–10625, 10.18653/V1/2024.FINDINGS-EMNLP.622
2024
Closest in time.
Ru D, Qiu L, Hu X, et al (2024) Ragchecker: A fine-grained framework for diagnosing retrieval-augmented generation. In: Advances in Neural Information Processing Systems 38 (NeurIPS)
2024
Closest in time.
Saad-Falcon J, Khattab O, Potts C, et al (2024) ARES: an automated evaluation framework for retrieval-augmented generation systems. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 338–354
2024
Closest in time.
2024
Closest in time.
Shi W, Min S, Yasunaga M, et al (2024) REPLUG: retrieval-augmented black-box language models. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp 8371–8384
2024
Closest in time.
Siino M, Tinnirello I (2024) GPT hallucination detection through prompt engineering. In: Working Notes of the Conference and Labs of the Evaluation Forum (CLEF), pp 712–721
2024
Closest in time.
Siino M, Tinnirello I, Cascia ML (2024) Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on transformers and traditional classifiers. Inf Syst 121:102342
2024
Closest in time.
Su W, Tang Y, Ai Q, et al (2024b) DRAGIN: dynamic retrieval augmented generation based on the real-time information needs of large language models. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), pp 12991–13013, 10.18653/V1/2024.ACL-LONG.702
2024
Closest in time.
Sun J, Xu C, Tang L, et al (2024) Think-on-graph: Deep and responsible reasoning of large language model on knowledge graph. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR)
2024
Closest in time.
Team L (2024) The llama 3 herd of models. CoRR abs/2407.21783
2024
Closest in time.
Vu T, Iyyer M, Wang X, et al (2024) Freshllms: Refreshing large language models with search engine augmentation. In: Findings of the Association for Computational Linguistics: ACL, pp 13697–13720, 10.18653/V1/2024.FINDINGS-ACL.813
2024
Closest in time.
2024
Closest in time.
Wu S, Xiong Y, Cui Y, et al (2024) Refusion: Improving natural language understanding with computation-efficient retrieval representation fusion. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR)
2024
Closest in time.
Xie J, Chen Z, Zhang R, et al (2024) Large multimodal agents: A survey. CoRR abs/2402.15116
2024
Closest in time.
Xiong G, Jin Q, Lu Z, et al (2024) Benchmarking retrieval-augmented generation for medicine. In: Findings of the Association for Computational Linguistics: ACL, pp 6233–6251
2024
Closest in time.
Xu F, Shi W, Choi E (2024) RECOMP: improving retrieval-augmented lms with context compression and selective augmentation. In: Proceedings of The Twelfth International Conference on Learning Representations (ICLR)
2024
Closest in time.
Yan S, Gu J, Zhu Y, et al (2024) Corrective retrieval augmented generation. CoRR abs/2401.15884
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
(2025a) Llm01:2025 prompt injection - owasp gen ai security project. URL https://genai.owasp.org/llmrisk/llm01-prompt-injection/
2025
Closest in time.
(2025b) Llm08:2025 vector and embedding weaknesses - owasp gen ai security project. URL https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/
2025
Closest in time.
Chang Z, Li M, Jia X, et al (2025) One shot dominance: Knowledge poisoning attack on retrieval-augmented generation systems. In: Findings of the Association for Computational Linguistics: EMNLP, pp 18811–18825
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
Jiang W, Subramanian S, Graves C, et al (2025) RAGO: systematic performance optimization for retrieval-augmented generation serving. In: Proceedings of the 52nd Annual International Symposium on Computer Architecture (ISCA), pp 974–989
2025
Closest in time.
Liang X, Niu S, Li Z, et al (2025) Saferag: Benchmarking security in retrieval-augmented generation of large language model. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 4609–4631
2025
Closest in time.
Martinelli I, Ponti MA (2025) Towards secure retrieval-augmented generation: Preventing LLM data leaks with RBAC. In: Proceedings of the 35th Brazilian Conference on Intelligent Systems (BRACIS), Lecture Notes in Computer Science, vol 16182. Springer, pp 500–515
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
2025
Closest in time.
Siino M, Tinnirello I, Cascia ML (2025) From foundations to GPT in text classification: A comprehensive survey on current approaches and future trends. Found Trends Inf Retr 19(5):557–711
2025
Closest in time.
2025
Closest in time.
Xi Z, Chen W, Guo X, et al (2025) The rise and potential of large language model based agents: a survey. Science China Information Sciences 68(2)
2025
Closest in time.
Xia S, Wang X, Liang J, et al (2025) Ground every sentence: Improving retrieval-augmented llms with interleaved reference-claim generation. In: Findings of the Association for Computational Linguistics (NAACL), pp 969–988
2025
Closest in time.
2025
Closest in time.
Yu X, Jian P, Chen C (2025) Tablerag: A retrieval augmented generation framework for heterogeneous document reasoning. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 14063–14082
2025
Closest in time.
Zeng S, Zhang J, He P, et al (2025) Mitigating the privacy issues in retrieval-augmented generation (RAG) via pure synthetic data. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 24527–24558
2025
Closest in time.
Zhang Z, Dai Q, Bo X, et al (2025) A survey on the memory mechanism of large language model-based agents. ACM Transactions on Information Systems 43(6):155:1–155:47
2025
Closest in time.
Zhu R, Liu X, Sun Z, et al (2025) Mitigating lost-in-retrieval problems in retrieval augmented multi-hop question answering. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL), pp 22362–22375
2025
Closest in time.
Zou W, Geng R, Wang B, et al (2025) Poisonedrag: Knowledge corruption attacks to retrieval-augmented generation of large language models. In: Proceedings of the 34th USENIX Security Symposium (USENIX Security), pp 3827–3844
2025
Closest in time.
2026
Closest in time.
Amazon Web Services (2026) Retrieving information from data sources using amazon bedrock knowledge bases. https://docs.aws.amazon.com/bedrock/latest/userguide/kb-how-retrieval.html , accessed: 2026-03-04
2026
Closest in time.
Chen Y, Xiong Y, Wu S, et al (2026) Refilter: Improving robustness of retrieval-augmented generation via gated filter. arXiv preprint arXiv:260212709
2026
Closest in time.
Databricks (2025a) Mosaic ai vector search. https://docs.databricks.com/gcp/en/vector-search/vector-search , accessed: 2026-03-04
2026
Closest in time.
deepset (2026) Haystack documentation. https://docs.haystack.deepset.ai/docs/get-started , accessed: 2026-03-04
2026
Closest in time.
DSPy (2026) Tutorial: Retrieval-augmented generation (rag) – dspy. https://dspy.ai/tutorials/rag/ , accessed: 2026-03-04
2026
Closest in time.
Google Cloud (2026a) Use data ingestion with vertex ai rag engine. https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/use-data-ingestion , accessed: 2026-03-04
2026
Closest in time.
Google Cloud (2026b) Vertex ai rag engine billing. https://docs.cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-engine-billing , accessed: 2026-03-04
2026
Closest in time.
LangChain (2026) Langsmith observability. https://docs.langchain.com/langsmith/observability , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2024) Retrieval-augmented generation (rag) applications with autogen. https://microsoft.github.io/autogen/0.2/blog/2023/10/18/RetrieveChat/ , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2025a) Adding retrieval augmented generation (rag) to semantic kernel agents. https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-rag?pivots=programming-language-csharp , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2025b) Get started with rag using a prompt flow sample. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-retrieval-augmented-generation?view=azureml-api-2 , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2025c) Overview of the microsoft 365 copilot retrieval api. https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/api/ai-services/retrieval/overview , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2026a) Agentic retrieval in azure ai search. https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-overview , accessed: 2026-03-04
2026
Closest in time.
Microsoft (2026b) Retrieval-augmented generation (rag) in azure ai search. https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview , accessed: 2026-03-04
2026
Closest in time.
OpenAI (2026) Evals – openai api reference. https://platform.openai.com/docs/api-reference/evals , accessed: 2026-03-04
2026
Closest in time.
Spring (2026) Retrieval augmented generation-spring ai. https://docs.spring.io/spring-ai/reference/api/retrieval-augmented-generation.html , accessed: 2026-03-04
2026
Closest in time.
Vercel (2026) Rag agent guide – vercel ai sdk. https://ai-sdk.dev/cookbook/guides/rag-chatbot , accessed: 2026-03-04
2026
Closest in time.
Min S, Shi W, Lewis M, et al (2023b) Nonparametric masked language modeling. In: Findings of the Association for Computational Linguistics: ACL, pp 2097–2118
2097
Closest in time.