Fetching the paper…
Reading the bibliography…
Identifying texts with a given semantics is central for many information seeking scenarios.
Okapi at trec-3
Stephen Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, and M. Gatford · 1995
Earlier work this paper cites.
Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych · 2004
Earlier work this paper cites.
The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini · 2005
Earlier work this paper cites.
Large scale online learning of image similarity through ranking
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio · 2010
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Senteval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Earlier work this paper cites.
A repository of conversational datasets
Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšic, Georgios Spithourakis, Pei-Hao Su, Ivan Vulic, et al · 2019
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Cited alongside, same era.
Global explainability of bert-based evaluation metrics by disentangling along linguistic factors
Marvin Kaster, Wei Zhao, and Steffen Eger · 2021
Later among the works it cites.
Precise zero-shot dense retrieval without relevance labels, 2022
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan · 2022
Later among the works it cites.
Unsupervised dense information retrieval with contrastive learning, 2022
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2022
Later among the works it cites.
Mteb: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2022
Later among the works it cites.
Sbert studies meaning representations: Decomposing sentence embeddings into explainable semantic features
Juri Opitz and Anette Frank · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Cited alongside, same era.
Mpnet: Masked and permuted pre-training for language understanding
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen · 2021
Cited alongside, same era.
Towards unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2021
Cited alongside, same era.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning
Cited in the paper.
Aaron Parisi, Yao Zhao, and Noah Fiedel · 2022
Later among the works it cites.
One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, Tao Yu, et al · 2022
Later among the works it cites.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
Later among the works it cites.
Towards general text embeddings with multi-stage contrastive learning
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang · 2023
Closest in time.
C-pack: Packaged resources to advance general chinese embedding, 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
Closest in time.