📄 Task-aware Retrieval with Instructions

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

87 papers of our library cite it (of the 140884 whose bibliographies we read); 15 of them have a page here, 72 not yet.

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• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
• Precise Zero-Shot Dense Retrieval without Relevance Labels
• Challenges and Applications of Large Language Models
• NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
• Towards General Text Embeddings with Multi-stage Contrastive Learning
• Augmented Language Models: a Survey
• Generative Representational Instruction Tuning
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
• Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering
• One Embedder, Any Task: Instruction-Finetuned Text Embeddings
• C-Pack: Packed Resources For General Chinese Embeddings
• IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions
• Large Language Models for Information Retrieval: A Survey

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2023.findings-acl.225

@inproceedings{asai2023task,
  title = {{Task-aware Retrieval with Instructions}},
  author = {Asai, Akari and Schick, Timo and Lewis, Patrick and Chen, Xilun and Izacard, Gautier and Riedel, Sebastian and Hajishirzi, Hannaneh and Yih, Wen-tau},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-acl.225},
  url = {https://doi.org/10.18653/v1/2023.findings-acl.225}
}

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