Meta-Task Prompting Elicits Embeddings from Large Language Models

Language models are few-shot learners

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SemEval-2012 task 6: A pilot on semantic textual similarity

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Semeval-2012 task 6: A pilot on semantic textual similarity

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Semeval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation

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Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation

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Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. 2017a · 2017

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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation

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Learning to match using local and distributed representations of text for web search

Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017 · 2017

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SentEval: An evaluation toolkit for universal sentence representations

Alexis Conneau and Douwe Kiela. 2018 · 2018

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BERT: Pre-training of deep bidirectional transformers for language understanding

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019

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Sentence-BERT: Sentence embeddings using Siamese BERT-networks

Nils Reimers and Iryna Gurevych. 2019 · 2019

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Finetuned language models are zero-shot learners

Original

Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022 · 2022

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PCL: Peer-contrastive learning with diverse augmentations for unsupervised sentence embeddings

Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, and Daxin Jiang. 2022 · 2022

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Opt: Open pre-trained transformer language models

Original

Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022

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Task-aware retrieval with instructions

Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. 2023 · 2023

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Improving contrastive learning of sentence embeddings from AI feedback