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We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings.
Language models are few-shot learners
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Ms marco: A human-generated machine reading comprehension dataset
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Dureader: a chinese machine reading comprehension dataset from real-world applications
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SentEval: An Evaluation Toolkit for Universal Sentence Representations
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Multi-Scale Attentive Interaction Networks for Chinese Medical Question Answer Selection
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Sentence-bert: Sentence embeddings using siamese bert-networks
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Approximate nearest neighbor negative contrastive learning for dense text retrieval
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CLUE: A Chinese language understanding evaluation benchmark
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mmarco: A multilingual version of the ms marco passage ranking dataset
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Condenser: a pre-training architecture for dense retrieval
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Scaling deep contrastive learning batch size under memory limited setup
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Sgpt: Gpt sentence embeddings for semantic search
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MTEB: Massive text embedding benchmark
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Crosslingual generalization through multitask finetuning
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Text and code embeddings by contrastive pre-training
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
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Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al · 2021
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Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2021
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Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models
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Finetuned language models are zero-shot learners
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Matching-oriented product quantization for ad-hoc retrieval
Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, and Xing Xie. 2021 · 2021
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Yifu Qiu, Hongyu Li, Yingqi Qu, Ying Chen, Qiaoqiao She, Jing Liu, Hua Wu, and Haifeng Wang. 2022 · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
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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
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Simlm: Pre-training with representation bottleneck for dense passage retrieval
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022a · 2022
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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. 2022b · 2022
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Progressively optimized bi-granular document representation for scalable embedding based retrieval. In Proceedings of the ACM Web Conference 2022 . 286–296
Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Yingxia Shao, Defu Lian, Chaozhuo Li, Hao Sun, Denvy Deng, Liangjie Zhang, et al · 2022
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Uni-retriever: Towards learning the unified embedding based retriever in bing sponsored search. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4493–4501
Jianjin Zhang, Zheng Liu, Weihao Han, Shitao Xiao, Ruicheng Zheng, Yingxia Shao, Hao Sun, Hanqing Zhu, Premkumar Srinivasan, Weiwei Deng, et al · 2022
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SantaCoder: don’t reach for the stars!
Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, et al · 2023
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Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
Ehsan Kamalloo, Nandan Thakur, Carlos Lassance, Xueguang Ma, Jheng-Hong Yang, and Jimmy Lin. 2023 · 2023
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StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
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Towards General Text Embeddings with Multi-stage Contrastive Learning
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. 2023b · 2023
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OctoPack: Instruction Tuning Code Large Language Models
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre. 2023a · 2023
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Scaling Data-Constrained Language Models
Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. 2023b · 2023
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ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al · 2023
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Replug: Retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023
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RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
Shitao Xiao, Zheng Liu, Yingxia Shao, and Zhao Cao. 2023 · 2023
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T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Xiaohui Xie, Qian Dong, Bingning Wang, Feiyang Lv, Ting Yao, Weinan Gan, Zhijing Wu, Xiangsheng Li, Haitao Li, Yiqun Liu, et al · 2023
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