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This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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The new york times annotated corpus
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A survey of text clustering algorithms
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Character-level convolutional networks for text classification
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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SentEval: An evaluation toolkit for universal sentence representations
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Learning and evaluating sparse interpretable sentence embeddings
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Unsupervised aspect-based multi-document abstractive summarization
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Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model
Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019 · 2019
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Word2Sense: Sparse interpretable word embeddings
Abhishek Panigrahi, Harsha Vardhan Simhadri, and Chiranjib Bhattacharyya. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019a · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019b · 2019
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C-STS: Conditional semantic textual similarity
Ameet Deshpande, Carlos Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, and Karthik Narasimhan. 2023 · 2023
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Language models represent space and time
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OpenAI. 2023 · 2023
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Generating efficient training data via llm-based attribute manipulation
Letian Peng, Yuwei Zhang, and Jingbo Shang. 2023 · 2023
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One embedder, any task: Instruction-finetuned text embeddings
Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2023 · 2023
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Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Scaling instruction-finetuned language models
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith Hall, Daniel Cer, and Yinfei Yang. 2022a · 2022
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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, and Yinfei Yang. 2022b · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Text embeddings by weakly-supervised contrastive pre-training
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023a
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Function vectors in large language models
Eric Todd, Millicent L. Li, Arnab Sen Sharma, Aaron Mueller, Byron C. Wallace, and David Bau. 2023 · 2023
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Goal-driven explainable clustering via language descriptions
Zihan Wang, Jingbo Shang, and Ruiqi Zhong. 2023 · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighof. 2023 · 2023
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ClusterLLM: Large language models as a guide for text clustering
Yuwei Zhang, Zihan Wang, and Jingbo Shang. 2023b · 2023
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MTEB: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loic Magne, and Nils Reimers. 2023 · 2037
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