---
title: "Task-aware Retrieval with Instructions — Asai 2023"
description: "A search query alone often hides what the user actually wants."
authors: ["Akari Asai", "Timo Schick", "Patrick Lewis", "Xilun Chen", "Gautier Izacard", "Sebastian Riedel", "Hannaneh Hajishirzi", "Wen-tau Yih"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: ACL 2023"
source: "https://aclanthology.org/2023.findings-acl.225/"
doi: "10.18653/v1/2023.findings-acl.225"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/"
---
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# Task-aware Retrieval with Instructions

**Authors:** [Akari Asai](https://md.research.elamur.ai/free/en/authors/akari-asai/), [Timo Schick](https://md.research.elamur.ai/free/en/authors/timo-schick/), [Patrick Lewis](https://md.research.elamur.ai/free/en/authors/patrick-lewis/), [Xilun Chen](https://md.research.elamur.ai/free/en/authors/xilun-chen/), [Gautier Izacard](https://md.research.elamur.ai/free/en/authors/gautier-izacard/), [Sebastian Riedel](https://md.research.elamur.ai/free/en/authors/sebastian-riedel/), [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/), [Wen-tau Yih](https://md.research.elamur.ai/free/en/authors/wen-tau-yih/) · **Year:** 2023 · **Venue:** Findings of the Association for Computational Linguistics: ACL 2023 · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-acl.225/) · [PDF](https://aclanthology.org/2023.findings-acl.225.pdf) · [DOI 10.18653/v1/2023.findings-acl.225](https://doi.org/10.18653/v1/2023.findings-acl.225) · [OpenAlex](https://openalex.org/W4385570569)

**Citations:** 44 citations (openalex-snapshot · 2026-10-14) · [87](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
A search query alone often hides what the user actually wants. The same query might call for code, an answer, or a duplicate question. Usually this is handled by training a separate retriever for each intent. Asai et al. instead propose "retrieval with instructions," where a natural-language description of the search task accompanies the query.

To support this, they built BERRI, a unified collection of 37 retrieval datasets across 10 domains. Some of these datasets were originally non-retrieval tasks recast as retrieval. Experts annotated each task with instructions that specify intent, domain and unit.

On BERRI they trained TART, a single multi-task retriever, in two variants: a dual-encoder (TART-dual) and a cross-encoder (TART-full). Training used hard negatives and new "instruction-unfollowing" negatives. TART-full set new state-of-the-art results on zero-shot BEIR and LOTTE, beating systems roughly three times larger. It also led on X²-Retrieval, a new pooled cross-task, cross-domain setup.

The work matters because one instruction-following model can replace many task-specific retrievers and adapt to new tasks without parameter updates.

## Key points
- **BERRI dataset:** 37 datasets were selected from over 60 candidates, drawn from KILT, the Sentence-Transformers embedding training data, ACL Anthology and Hugging Face. On average, each task has about 3.5 expert-written instructions, totaling more than 100.
- **Instruction schema:** every instruction states three things:
  - the intent (how the target text relates to the query),
  - the domain (e.g., Wikipedia, PubMed),
  - the unit (e.g., sentence, paragraph).
- **Model variants:** TART-dual is a dense dual-encoder with about 110M parameters. TART-full is a cross-encoder built on T5 LM-Adapt (base, large and XL) that reranks the top 100 Contriever results. Larger sizes performed clearly better.
- **Ablations:**
  - Removing instructions at training or test time hurts results.
  - Dropping instruction-unfollowing negatives leaves BEIR performance roughly unchanged but lowers X²-Retrieval performance.
  - Training on more datasets helps.
- **Limitations and release:** TART-dual sometimes falls behind its non-instruction counterpart. Code and models are released at github.com/facebookresearch/tart.

*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](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

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.

- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
- [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/)
- [Challenges and Applications of Large Language Models](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
- [NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://md.research.elamur.ai/free/en/papers/li-2023-towards-general-text-embeddings-multi/)
- [Augmented Language Models: a Survey](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
- [Generative Representational Instruction Tuning](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://md.research.elamur.ai/free/en/papers/qin-2024-large-language-models-are-effective/)
- [Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
- [Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering](https://md.research.elamur.ai/free/en/papers/srikanth-2024-pregnant-questions-importance-pragmatic-awareness/)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/)
- [IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/)
- [Large Language Models for Information Retrieval: A Survey](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@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}
}
```

- [← previous TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://md.research.elamur.ai/free/en/papers/kadaoui-2023-tarjamat-evaluation-bard-chatgpt-machine/)
- [next → Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing](https://md.research.elamur.ai/free/en/papers/li-2023-task-aware-self-supervised-framework/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/7/)

## References

89 references, 38 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. Open-domain question answering goes conversational via question rewriting. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics… [find](https://scholar.google.com/scholar?q=Raviteja+Anantha%2C+Svitlana+Vakulenko%2C+Zhucheng+Tu%2C+Shayne+Longpre%2C+Stephen+Pulman%2C+and+Srinivas+Chappidi%2E+2021%2E+Open%2Ddomain+question+answering+goes+conversational+via+question+rewriting%2E+Proceedings+o) [our card](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/)
2. Akari Asai, Xinyan Yu, Jungo Kasai, and Hanna Hajishirzi. 2021. One question answering model for many languages with cross-lingual dense passage retrieval. Proceedings of Advances in Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Akari+Asai%2C+Xinyan+Yu%2C+Jungo+Kasai%2C+and+Hanna+Hajishirzi%2E+2021%2E+One+question+answering+model+for+many+languages+with+cross%2Dlingual+dense+passage+retrieval%2E+Proceedings+of+Advances+in+Neural+Informatio)
3. Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya… [find](https://scholar.google.com/scholar?q=Stephen+Bach%2C+Victor+Sanh%2C+Zheng+Xin+Yong%2C+Albert+Webson%2C+Colin+Raffel%2C+Nihal+V%2E+Nayak%2C+Abheesht+Sharma%2C+Taewoon+Kim%2C+M+Saiful+Bari%2C+Thibault+Fevry%2C+Zaid+Alyafeai%2C+Manan+Dey%2C+Andrea+Santilli%2C+Zhiqing+)
4. Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2016. MS MARCO: A human generated machine reading comprehension dataset. [find](https://scholar.google.com/scholar?q=Payal+Bajaj%2C+Daniel+Campos%2C+Nick+Craswell%2C+Li+Deng%2C+Jianfeng+Gao%2C+Xiaodong+Liu%2C+Rangan+Majumder%2C+Andrew+McNamara%2C+Bhaskar+Mitra%2C+Tri+Nguyen%2C+Mir+Rosenberg%2C+Xia+Song%2C+Alina+Stoica%2C+Saurabh+Tiwary%2C+and+) [our card](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/)
5. Alexander Bondarenko, Maik Fröbe, Meriem Beloucif, Lukas Gienapp, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, and Matthias Hagen. 2020. Overview of touché 2020: Argument retrieval: Extended abstract. In Proceedings of Experimental IR Meets Multi… [find](https://scholar.google.com/scholar?q=Alexander+Bondarenko%2C+Maik+Fr%C3%B6be%2C+Meriem+Beloucif%2C+Lukas+Gienapp%2C+Yamen+Ajjour%2C+Alexander+Panchenko%2C+Chris+Biemann%2C+Benno+Stein%2C+Henning+Wachsmuth%2C+Martin+Potthast%2C+and+Matthias+Hagen%2E+2020%2E+Overview+)
6. Vera Boteva, Demian Gholipour, Artem Sokolov, and Stefan Riezler. 2016. A full-text learning to rank dataset for medical information retrieval. In Proceedings of the 38th European Conference on Information Retrieval. [find](https://scholar.google.com/scholar?q=Vera+Boteva%2C+Demian+Gholipour%2C+Artem+Sokolov%2C+and+Stefan+Riezler%2E+2016%2E+A+full%2Dtext+learning+to+rank+dataset+for+medical+information+retrieval%2E+In+Proceedings+of+the+38th+European+Conference+on+Inform)
7. Isabel Cachola, Kyle Lo, Arman Cohan, and Daniel Weld. 2020. TLDR: Extreme summarization of scientific documents. In Findings of the Association for Computational Linguistics: EMNLP 2020. [find](https://scholar.google.com/scholar?q=Isabel+Cachola%2C+Kyle+Lo%2C+Arman+Cohan%2C+and+Daniel+Weld%2E+2020%2E+TLDR%3A+Extreme+summarization+of+scientific+documents%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2020%2E) [our card](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/)
8. Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, and Wen-tau Yih. 2022. Salient phrase aware dense retrieval: Can a dense retriever imitate a sparse one? In Proceedings of the 2022 Conference on Empirical Methods in Natural Languag… [find](https://scholar.google.com/scholar?q=Xilun+Chen%2C+Kushal+Lakhotia%2C+Barlas+Og%CC%86uz%2C+Anchit+Gupta%2C+Patrick+Lewis%2C+Stan+Peshterliev%2C+Yashar+Mehdad%2C+Sonal+Gupta%2C+and+Wen%2Dtau+Yih%2E+2022%2E+Salient+phrase+aware+dense+retrieval%3A+Can+a+dense+retriever)
9. Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick. 2015. Microsoft coco captions: Data collection and evaluation server. [find](https://scholar.google.com/scholar?q=Xinlei+Chen%2C+Hao+Fang%2C+Tsung%2DYi+Lin%2C+Ramakrishna+Vedantam%2C+Saurabh+Gupta%2C+Piotr+Doll%C3%A1r%2C+and+C+Lawrence+Zitnick%2E+2015%2E+Microsoft+coco+captions%3A+Data+collection+and+evaluation+server%2E) [our card](https://md.research.elamur.ai/free/en/papers/chen-2015-microsoft-coco-captions-data-collection/)
10. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, A… [find](https://scholar.google.com/scholar?q=Hyung+Won+Chung%2C+Le+Hou%2C+Shayne+Longpre%2C+Barret+Zoph%2C+Yi+Tay%2C+William+Fedus%2C+Eric+Li%2C+Xuezhi+Wang%2C+Mostafa+Dehghani%2C+Siddhartha+Brahma%2C+Albert+Webson%2C+Shixiang+Shane+Gu%2C+Zhuyun+Dai%2C+Mirac+Suzgun%2C+Xiny)
11. Arman Cohan, Sergey Feldman, Iz Beltagy, Doug Downey, and Daniel Weld. 2020. SPECTER: Document-level representation learning using citation-informed transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Arman+Cohan%2C+Sergey+Feldman%2C+Iz+Beltagy%2C+Doug+Downey%2C+and+Daniel+Weld%2E+2020%2E+SPECTER%3A+Document%2Dlevel+representation+learning+using+citation%2Dinformed+transformers%2E+In+Proceedings+of+the+58th+Annual+Mee) [our card](https://md.research.elamur.ai/free/en/papers/cohan-2020-specter-document-level-representation-learning/)
12. Zhuyun Dai and Jamie Callan. 2019. Deeper text understanding for IR with contextual neural language modeling. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. [find](https://scholar.google.com/scholar?q=Zhuyun+Dai+and+Jamie+Callan%2E+2019%2E+Deeper+text+understanding+for+IR+with+contextual+neural+language+modeling%2E+In+Proceedings+of+the+42nd+International+ACM+SIGIR+Conference+on+Research+and+Development+)
13. Zhuyun Dai and Jamie Callan. 2020. Context-aware document term weighting for ad-hoc search. In Proceedings of The Web Conference 2020. [find](https://scholar.google.com/scholar?q=Zhuyun+Dai+and+Jamie+Callan%2E+2020%2E+Context%2Daware+document+term+weighting+for+ad%2Dhoc+search%2E+In+Proceedings+of+The+Web+Conference+2020%2E)
14. Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, and Ming-Wei Chang. 2022. Promptagator: Few-shot dense retrieval from 8 examples. [find](https://scholar.google.com/scholar?q=Zhuyun+Dai%2C+Vincent+Y%2E+Zhao%2C+Ji+Ma%2C+Yi+Luan%2C+Jianmo+Ni%2C+Jing+Lu%2C+Anton+Bakalov%2C+Kelvin+Guu%2C+Keith+B%2E+Hall%2C+and+Ming%2DWei+Chang%2E+2022%2E+Promptagator%3A+Few%2Dshot+dense+retrieval+from+8+examples%2E) [our card](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/)
15. Ashwin Devaraj, Iain Marshall, Byron Wallace, and Junyi Jessy Li. 2021. Paragraph-level simplification of medical texts. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Ashwin+Devaraj%2C+Iain+Marshall%2C+Byron+Wallace%2C+and+Junyi+Jessy+Li%2E+2021%2E+Paragraph%2Dlevel+simplification+of+medical+texts%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+of+the+Asso) [our card](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/)
16. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2019%2E+BERT%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+In+Proceedings+of+the+2019+Conference+of+the+N)
17. Thomas Diggelmann, Jordan L. Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, and Markus Leippold. 2020. CLIMATE-FEVER: A dataset for verification of real-world climate claims. In Proceedings of Tackling Climate Change with Machine Learning Workshop at NeurIPS. [find](https://scholar.google.com/scholar?q=Thomas+Diggelmann%2C+Jordan+L%2E+Boyd%2DGraber%2C+Jannis+Bulian%2C+Massimiliano+Ciaramita%2C+and+Markus+Leippold%2E+2020%2E+CLIMATE%2DFEVER%3A+A+dataset+for+verification+of+real%2Dworld+climate+claims%2E+In+Proceedings+of+Ta) [our card](https://md.research.elamur.ai/free/en/papers/diggelmann-2020-climate-fever-dataset-verification-real/)
18. Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2019. Wizard of wikipedia: Knowledge-powered conversational agents. In Proceedings of International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Emily+Dinan%2C+Stephen+Roller%2C+Kurt+Shuster%2C+Angela+Fan%2C+Michael+Auli%2C+and+Jason+Weston%2E+2019%2E+Wizard+of+wikipedia%3A+Knowledge%2Dpowered+conversational+agents%2E+In+Proceedings+of+International+Conference+on)
19. Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017. SearchQA: A new q&a dataset augmented with context from a search engine. [find](https://scholar.google.com/scholar?q=Matthew+Dunn%2C+Levent+Sagun%2C+Mike+Higgins%2C+V+Ugur+Guney%2C+Volkan+Cirik%2C+and+Kyunghyun+Cho%2E+2017%2E+SearchQA%3A+A+new+q%26a+dataset+augmented+with+context+from+a+search+engine%2E) [our card](https://md.research.elamur.ai/free/en/papers/dunn-2017-searchqa-new-q-dataset-augmented/)
20. Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014. Open question answering over curated and extracted knowledge bases. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [find](https://scholar.google.com/scholar?q=Anthony+Fader%2C+Luke+Zettlemoyer%2C+and+Oren+Etzioni%2E+2014%2E+Open+question+answering+over+curated+and+extracted+knowledge+bases%2E+In+Proceedings+of+the+20th+ACM+SIGKDD+International+Conference+on+Knowledge)
21. Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long form question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Angela+Fan%2C+Yacine+Jernite%2C+Ethan+Perez%2C+David+Grangier%2C+Jason+Weston%2C+and+Michael+Auli%2E+2019%2E+ELI5%3A+Long+form+question+answering%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Comp) [our card](https://md.research.elamur.ai/free/en/papers/fan-2019-eli5-long-form-question-answering/)
22. Katja Filippova and Yasemin Altun. 2013. Overcoming the lack of parallel data in sentence compression. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Katja+Filippova+and+Yasemin+Altun%2E+2013%2E+Overcoming+the+lack+of+parallel+data+in+sentence+compression%2E+In+Proceedings+of+the+2013+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2E)
23. Antonio Gulli. 2004. Ag’s corpus of news articles. [find](https://scholar.google.com/scholar?q=Antonio+Gulli%2E+2004%2E+Ag%E2%80%99s+corpus+of+news+articles%2E)
24. Felix Hamborg, Norman Meuschke, Corinna Breitinger, and Bela Gipp. 2017. news-please: A generic news crawler and extractor. In Proceedings of the 15th International Symposium of Information Science. [find](https://scholar.google.com/scholar?q=Felix+Hamborg%2C+Norman+Meuschke%2C+Corinna+Breitinger%2C+and+Bela+Gipp%2E+2017%2E+news%2Dplease%3A+A+generic+news+crawler+and+extractor%2E+In+Proceedings+of+the+15th+International+Symposium+of+Information+Science%2E)
25. Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan. 2017. Dbpedia-entity v2: A test collection for entity search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. [find](https://scholar.google.com/scholar?q=Faegheh+Hasibi%2C+Fedor+Nikolaev%2C+Chenyan+Xiong%2C+Krisztian+Balog%2C+Svein+Erik+Bratsberg%2C+Alexander+Kotov%2C+and+Jamie+Callan%2E+2017%2E+Dbpedia%2Dentity+v2%3A+A+test+collection+for+entity+search%2E+In+Proceedings+of)
26. Christopher Hidey and Kathy McKeown. 2016. Identifying causal relations using parallel Wikipedia articles. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Christopher+Hidey+and+Kathy+McKeown%2E+2016%2E+Identifying+causal+relations+using+parallel+Wikipedia+articles%2E+In+Proceedings+of+the+54th+Annual+Meeting+of+the+Association+for+Computational+Linguistics%2E)
27. Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. [find](https://scholar.google.com/scholar?q=Hamel+Husain%2C+Ho%2DHsiang+Wu%2C+Tiferet+Gazit%2C+Miltiadis+Allamanis%2C+and+Marc+Brockschmidt%2E+2019%2E+Codesearchnet+challenge%3A+Evaluating+the+state+of+semantic+code+search%2E) [our card](https://md.research.elamur.ai/free/en/papers/husain-2020-codesearchnet-challenge-evaluating-state-semantic/)
28. Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Gautier+Izacard%2C+Mathilde+Caron%2C+Lucas+Hosseini%2C+Sebastian+Riedel%2C+Piotr+Bojanowski%2C+Armand+Joulin%2C+and+Edouard+Grave%2E+2022%2E+Unsupervised+dense+information+retrieval+with+contrastive+learning%2E+Transac) [our card](https://md.research.elamur.ai/free/en/papers/izacard-2021-unsupervised-dense-information-retrieval-contrastive/)
29. Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019. PubMedQA: A dataset for biomedical research question answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Proce… [find](https://scholar.google.com/scholar?q=Qiao+Jin%2C+Bhuwan+Dhingra%2C+Zhengping+Liu%2C+William+Cohen%2C+and+Xinghua+Lu%2E+2019%2E+PubMedQA%3A+A+dataset+for+biomedical+research+question+answering%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods) [our card](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/)
30. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mandar+Joshi%2C+Eunsol+Choi%2C+Daniel+Weld%2C+and+Luke+Zettlemoyer%2E+2017%2E+TriviaQA%3A+A+large+scale+distantly+supervised+challenge+dataset+for+reading+comprehension%2E+In+Proceedings+of+the+55th+Annual+Meeting+) [our card](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
31. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Vladimir+Karpukhin%2C+Barlas+Oguz%2C+Sewon+Min%2C+Patrick+Lewis%2C+Ledell+Wu%2C+Sergey+Edunov%2C+Danqi+Chen%2C+and+Wen%2Dtau+Yih%2E+2020%2E+Dense+passage+retrieval+for+open%2Ddomain+question+answering%2E+In+Proceedings+of+th) [our card](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
32. Daniel Khashabi, Amos Ng, Tushar Khot, Ashish Sabharwal, Hannaneh Hajishirzi, and Chris Callison-Burch. 2021. GooAQ: Open question answering with diverse answer types. In Findings of the Association for Computational Linguistics: EMNLP 2021. [find](https://scholar.google.com/scholar?q=Daniel+Khashabi%2C+Amos+Ng%2C+Tushar+Khot%2C+Ashish+Sabharwal%2C+Hannaneh+Hajishirzi%2C+and+Chris+Callison%2DBurch%2E+2021%2E+GooAQ%3A+Open+question+answering+with+diverse+answer+types%2E+In+Findings+of+the+Association+f)
33. Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and effective passage search via contextualized late interaction over bert. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. [find](https://scholar.google.com/scholar?q=Omar+Khattab+and+Matei+Zaharia%2E+2020%2E+ColBERT%3A+Efficient+and+effective+passage+search+via+contextualized+late+interaction+over+bert%2E+In+Proceedings+of+the+43rd+International+ACM+SIGIR+Conference+on+Re) [our card](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/)
34. Mahnaz Koupaee and William Yang Wang. 2018. Wikihow: A large scale text summarization dataset. [find](https://scholar.google.com/scholar?q=Mahnaz+Koupaee+and+William+Yang+Wang%2E+2018%2E+Wikihow%3A+A+large+scale+text+summarization+dataset%2E)
35. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural… [find](https://scholar.google.com/scholar?q=Tom+Kwiatkowski%2C+Jennimaria+Palomaki%2C+Olivia+Redfield%2C+Michael+Collins%2C+Ankur+Parikh%2C+Chris+Alberti%2C+Danielle+Epstein%2C+Illia+Polosukhin%2C+Matthew+Kelcey%2C+Jacob+Devlin%2C+Kenton+Lee%2C+Kristina+N%2E+Toutanova) [our card](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
36. Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019. Latent retrieval for weakly supervised open domain question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kenton+Lee%2C+Ming%2DWei+Chang%2C+and+Kristina+Toutanova%2E+2019%2E+Latent+retrieval+for+weakly+supervised+open+domain+question+answering%2E+In+Proceedings+of+the+57th+Annual+Meeting+of+the+Association+for+Comput) [our card](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
37. Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, and Sebastian Riedel. 2021. PAQ: 65 million probably-asked questions and what you can do with them. Transactions of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Patrick+Lewis%2C+Yuxiang+Wu%2C+Linqing+Liu%2C+Pasquale+Minervini%2C+Heinrich+K%C3%BCttler%2C+Aleksandra+Piktus%2C+Pontus+Stenetorp%2C+and+Sebastian+Riedel%2E+2021%2E+PAQ%3A+65+million+probably%2Dasked+questions+and+what+you+can) [our card](https://md.research.elamur.ai/free/en/papers/lewis-2021-paq-65-million-probably-asked/)
38. Frederick Liu, Siamak Shakeri, Hongkun Yu, and Jing Li. 2021. EncT5: Fine-tuning t5 encoder for non-autoregressive tasks. [find](https://scholar.google.com/scholar?q=Frederick+Liu%2C+Siamak+Shakeri%2C+Hongkun+Yu%2C+and+Jing+Li%2E+2021%2E+EncT5%3A+Fine%2Dtuning+t5+encoder+for+non%2Dautoregressive+tasks%2E)
39. Xueqing Liu, Chi Wang, Yue Leng, and ChengXiang Zhai. 2018. LinkSO: A dataset for learning to retrieve similar question answer pairs on software development forums. In Proceedings of the 4th ACM SIGSOFT International Workshop on NLP for Software Engineering. [find](https://scholar.google.com/scholar?q=Xueqing+Liu%2C+Chi+Wang%2C+Yue+Leng%2C+and+ChengXiang+Zhai%2E+2018%2E+LinkSO%3A+A+dataset+for+learning+to+retrieve+similar+question+answer+pairs+on+software+development+forums%2E+In+Proceedings+of+the+4th+ACM+SIGSO)
40. Man Luo, Arindam Mitra, Tejas Gokhale, and Chitta Baral. 2022. Improving biomedical information retrieval with neural retrievers. In Proceedings of the AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Man+Luo%2C+Arindam+Mitra%2C+Tejas+Gokhale%2C+and+Chitta+Baral%2E+2022%2E+Improving+biomedical+information+retrieval+with+neural+retrievers%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence%2E)
41. Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018. WWW’18 open challenge: Financial opinion mining and question answering. In Companion Proceedings of the The Web Conference 2018. [find](https://scholar.google.com/scholar?q=Macedo+Maia%2C+Siegfried+Handschuh%2C+Andr%C3%A9+Freitas%2C+Brian+Davis%2C+Ross+McDermott%2C+Manel+Zarrouk%2C+and+Alexandra+Balahur%2E+2018%2E+WWW%E2%80%9918+open+challenge%3A+Financial+opinion+mining+and+question+answering%2E+In+Com)
42. Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, and Gargi Ghosh. 2021. Multi-task retrieval for knowledge-intensive tasks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confere… [find](https://scholar.google.com/scholar?q=Jean+Maillard%2C+Vladimir+Karpukhin%2C+Fabio+Petroni%2C+Wen%2Dtau+Yih%2C+Barlas+Oguz%2C+Veselin+Stoyanov%2C+and+Gargi+Ghosh%2E+2021%2E+Multi%2Dtask+retrieval+for+knowledge%2Dintensive+tasks%2E+In+Proceedings+of+the+59th+Annu)
43. Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022. MetaICL: Learning to learn in context. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=Sewon+Min%2C+Mike+Lewis%2C+Luke+Zettlemoyer%2C+and+Hannaneh+Hajishirzi%2E+2022%2E+MetaICL%3A+Learning+to+learn+in+context%2E+In+Proceedings+of+the+2022+Conference+of+the+North+American+Chapter+of+the+Association+fo)
44. Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. AmbigQA: Answering ambiguous open-domain questions. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Sewon+Min%2C+Julian+Michael%2C+Hannaneh+Hajishirzi%2C+and+Luke+Zettlemoyer%2E+2020%2E+AmbigQA%3A+Answering+ambiguous+open%2Ddomain+questions%2E+In+Proceedings+of+the+2020+Conference+on+Empirical+Methods+in+Natural+La)
45. Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022. Cross-task generalization via natural language crowdsourcing instructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3470–3487, Dublin, Irelan… [find](https://scholar.google.com/scholar?q=Swaroop+Mishra%2C+Daniel+Khashabi%2C+Chitta+Baral%2C+and+Hannaneh+Hajishirzi%2E+2022%2E+Cross%2Dtask+generalization+via+natural+language+crowdsourcing+instructions%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+th)
46. Stefano Mizzaro. 1998. How many relevances in information retrieval? Interacting with Computers. [find](https://scholar.google.com/scholar?q=Stefano+Mizzaro%2E+1998%2E+How+many+relevances+in+information+retrieval%3F+Interacting+with+Computers%2E)
47. Niklas Muennighoff. 2022. SGPT: Gpt sentence embeddings for semantic search. [find](https://scholar.google.com/scholar?q=Niklas+Muennighoff%2E+2022%2E+SGPT%3A+Gpt+sentence+embeddings+for+semantic+search%2E) [our card](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
48. Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Shashi+Narayan%2C+Shay+B%2E+Cohen%2C+and+Mirella+Lapata%2E+2018%2E+Don%E2%80%99t+give+me+the+details%2C+just+the+summary%21+topic%2Daware+convolutional+neural+networks+for+extreme+summarization%2E+In+Proceedings+of+the+2018+Co)
49. 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. Large dual encoders are generalizable retrievers. [find](https://scholar.google.com/scholar?q=Jianmo+Ni%2C+Chen+Qu%2C+Jing+Lu%2C+Zhuyun+Dai%2C+Gustavo+Hern%C3%A1ndez+%C3%81brego%2C+Ji+Ma%2C+Vincent+Y+Zhao%2C+Yi+Luan%2C+Keith+B+Hall%2C+Ming%2DWei+Chang%2C+et+al%2E+2021%2E+Large+dual+encoders+are+generalizable+retrievers%2E) [our card](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/)
50. Rodrigo Nogueira and Kyunghyun Cho. 2019. Passage re-ranking with bert. [find](https://scholar.google.com/scholar?q=Rodrigo+Nogueira+and+Kyunghyun+Cho%2E+2019%2E+Passage+re%2Dranking+with+bert%2E) [our card](https://md.research.elamur.ai/free/en/papers/nogueira-2019-passage-re-ranking-bert/)
51. Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Findings of the Association for Computational Linguistics: EMNLP 2020. [find](https://scholar.google.com/scholar?q=Rodrigo+Nogueira%2C+Zhiying+Jiang%2C+Ronak+Pradeep%2C+and+Jimmy+Lin%2E+2020%2E+Document+ranking+with+a+pre%2Dtrained+sequence%2Dto%2Dsequence+model%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP)
52. Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document expansion by query prediction. [find](https://scholar.google.com/scholar?q=Rodrigo+Nogueira%2C+Wei+Yang%2C+Jimmy+Lin%2C+and+Kyunghyun+Cho%2E+2019%2E+Document+expansion+by+query+prediction%2E) [our card](https://md.research.elamur.ai/free/en/papers/nogueira-2019-document-expansion-query-prediction/)
53. Long Ouyang, Jeff 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 Christiano, Jan Leike, and Ryan Lowe. 2022. Train… [find](https://scholar.google.com/scholar?q=Long+Ouyang%2C+Jeff+Wu%2C+Xu+Jiang%2C+Diogo+Almeida%2C+Carroll+L%2E+Wainwright%2C+Pamela+Mishkin%2C+Chong+Zhang%2C+Sandhini+Agarwal%2C+Katarina+Slama%2C+Alex+Ray%2C+John+Schulman%2C+Jacob+Hilton%2C+Fraser+Kelton%2C+Luke+Miller%2C+)
54. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022. Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering. In Proceedings of the Conference on Health, Inference, and Learning. [find](https://scholar.google.com/scholar?q=Ankit+Pal%2C+Logesh+Kumar+Umapathi%2C+and+Malaikannan+Sankarasubbu%2E+2022%2E+Medmcqa%3A+A+large%2Dscale+multi%2Dsubject+multi%2Dchoice+dataset+for+medical+domain+question+answering%2E+In+Proceedings+of+the+Conference+) [our card](https://md.research.elamur.ai/free/en/papers/pal-2022-medmcqa-large-scale-multi-subject/)
55. Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021. KILT: a benchmark for knowledge intensive language tasks. In Proceedings of th… [find](https://scholar.google.com/scholar?q=Fabio+Petroni%2C+Aleksandra+Piktus%2C+Angela+Fan%2C+Patrick+Lewis%2C+Majid+Yazdani%2C+Nicola+De+Cao%2C+James+Thorne%2C+Yacine+Jernite%2C+Vladimir+Karpukhin%2C+Jean+Maillard%2C+Vassilis+Plachouras%2C+Tim+Rockt%C3%A4schel%2C+and+Se) [our card](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
56. Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Grave, Wen-tau Yih, and Sebastian Riedel. 2021. The web is your oyster–knowledge-intensive nlp against a very large web corpus. pushshift. 2021. Npr corpus. [find](https://scholar.google.com/scholar?q=Aleksandra+Piktus%2C+Fabio+Petroni%2C+Vladimir+Karpukhin%2C+Dmytro+Okhonko%2C+Samuel+Broscheit%2C+Gautier+Izacard%2C+Patrick+Lewis%2C+Barlas+Og%CC%86uz%2C+Edouard+Grave%2C+Wen%2Dtau+Yih%2C+and+Sebastian+Riedel%2E+2021%2E+The+web+is)
57. Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021. RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. In Proceedings of the 2021 Conference of the North American Chapter of the Ass… [find](https://scholar.google.com/scholar?q=Yingqi+Qu%2C+Yuchen+Ding%2C+Jing+Liu%2C+Kai+Liu%2C+Ruiyang+Ren%2C+Wayne+Xin+Zhao%2C+Daxiang+Dong%2C+Hua+Wu%2C+and+Haifeng+Wang%2E+2021%2E+RocketQA%3A+An+optimized+training+approach+to+dense+passage+retrieval+for+open%2Ddomai) [our card](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/)
58. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Pranav+Rajpurkar%2C+Jian+Zhang%2C+Konstantin+Lopyrev%2C+and+Percy+Liang%2E+2016%2E+SQuAD%3A+100%2C000%2B+questions+for+machine+comprehension+of+text%2E+In+Proceedings+of+the+2016+Conference+on+Empirical+Methods+in+Natu) [our card](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
59. Soumik Rakshit. 2019. Yahoo answers dataset. [find](https://scholar.google.com/scholar?q=Soumik+Rakshit%2E+2019%2E+Yahoo+answers+dataset%2E)
60. Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. [find](https://scholar.google.com/scholar?q=Nils+Reimers+and+Iryna+Gurevych%2E+2019%2E+Sentence%2DBERT%3A+Sentence+embeddings+using+Siamese+BERT%2Dnetworks%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing+and+the)
61. Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends in Information Retrieval. [find](https://scholar.google.com/scholar?q=Stephen+Robertson+and+Hugo+Zaragoza%2E+2009%2E+The+probabilistic+relevance+framework%3A+Bm25+and+beyond%2E+Foundations+and+Trends+in+Information+Retrieval%2E)
62. Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, and Rodrigo Nogueira. 2022. No parameter left behind: How distillation and model size affect zero-shot retrieval. [find](https://scholar.google.com/scholar?q=Guilherme+Moraes+Rosa%2C+Luiz+Bonifacio%2C+Vitor+Jeronymo%2C+Hugo+Abonizio%2C+Marzieh+Fadaee%2C+Roberto+Lotufo%2C+and+Rodrigo+Nogueira%2E+2022%2E+No+parameter+left+behind%3A+How+distillation+and+model+size+affect+zero%2D)
63. Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Alexander+M%2E+Rush%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+2015%2E+A+neural+attention+model+for+abstractive+sentence+summarization%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Language)
64. Ian Ruthven and Mounia Lalmas. 2003. A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review. [find](https://scholar.google.com/scholar?q=Ian+Ruthven+and+Mounia+Lalmas%2E+2003%2E+A+survey+on+the+use+of+relevance+feedback+for+information+access+systems%2E+The+Knowledge+Engineering+Review%2E)
65. Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022. Improving passage retrieval with zero-shot question generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Devendra+Singh+Sachan%2C+Mike+Lewis%2C+Mandar+Joshi%2C+Armen+Aghajanyan%2C+Wen%2Dtau+Yih%2C+Joelle+Pineau%2C+and+Luke+Zettlemoyer%2E+2022%2E+Improving+passage+retrieval+with+zero%2Dshot+question+generation%2E+In+Proceeding) [our card](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
66. Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chan… [find](https://scholar.google.com/scholar?q=Victor+Sanh%2C+Albert+Webson%2C+Colin+Raffel%2C+Stephen+Bach%2C+Lintang+Sutawika%2C+Zaid+Alyafeai%2C+Antoine+Chaffin%2C+Arnaud+Stiegler%2C+Arun+Raja%2C+Manan+Dey%2C+M+Saiful+Bari%2C+Canwen+Xu%2C+Urmish+Thakker%2C+Shanya+Sharma) [our card](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
67. Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2022. Col-BERTv2: Effective and efficient retrieval via lightweight late interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lan… [find](https://scholar.google.com/scholar?q=Keshav+Santhanam%2C+Omar+Khattab%2C+Jon+Saad%2DFalcon%2C+Christopher+Potts%2C+and+Matei+Zaharia%2E+2022%2E+Col%2DBERTv2%3A+Effective+and+efficient+retrieval+via+lightweight+late+interaction%2E+In+Proceedings+of+the+2022+)
68. Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). [find](https://scholar.google.com/scholar?q=Abigail+See%2C+Peter+J%2E+Liu%2C+and+Christopher+D%2E+Manning%2E+2017%2E+Get+to+the+point%3A+Summarization+with+pointer%2Dgenerator+networks%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computati)
69. Kornrel Csernai Shankar Iyer, Nikhil Dandekar. 2012. First quora dataset release: Question pairs. [find](https://scholar.google.com/scholar?q=Kornrel+Csernai+Shankar+Iyer%2C+Nikhil+Dandekar%2E+2012%2E+First+quora+dataset+release%3A+Question+pairs%2E)
70. Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger, and Doug Downey. 2022. Multi-lexsum: Real-world summaries of civil rights lawsuits at multiple granularities. In Proceedings of the 36th Conference on Neural Information Processing Systems Datasets and Benchmarks Track. [find](https://scholar.google.com/scholar?q=Zejiang+Shen%2C+Kyle+Lo%2C+Lauren+Yu%2C+Nathan+Dahlberg%2C+Margo+Schlanger%2C+and+Doug+Downey%2E+2022%2E+Multi%2Dlexsum%3A+Real%2Dworld+summaries+of+civil+rights+lawsuits+at+multiple+granularities%2E+In+Proceedings+of+the+)
71. Amit Singhal et al. 2001. Modern information retrieval: A brief overview. IEEE Data Engineering Bulletin. [find](https://scholar.google.com/scholar?q=Amit+Singhal+et+al%2E+2001%2E+Modern+information+retrieval%3A+A+brief+overview%2E+IEEE+Data+Engineering+Bulletin%2E)
72. Hongjin Su, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, Tao Yu, et al. 2022. One embedder, any task: Instruction-finetuned text embeddings. arXiv preprint arXiv:2212.09741. [find](https://scholar.google.com/scholar?q=Hongjin+Su%2C+Jungo+Kasai%2C+Yizhong+Wang%2C+Yushi+Hu%2C+Mari+Ostendorf%2C+Wen%2Dtau+Yih%2C+Noah+A+Smith%2C+Luke+Zettlemoyer%2C+Tao+Yu%2C+et+al%2E+2022%2E+One+embedder%2C+any+task%3A+Instruction%2Dfinetuned+text+embeddings%2E+arXiv+) [our card](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
73. Robert S. Taylor. 1962. The process of asking questions. American Documentation. [find](https://scholar.google.com/scholar?q=Robert+S%2E+Taylor%2E+1962%2E+The+process+of+asking+questions%2E+American+Documentation%2E)
74. Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Proceedings of the 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track. [find](https://scholar.google.com/scholar?q=Nandan+Thakur%2C+Nils+Reimers%2C+Andreas+R%C3%BCckl%C3%A9%2C+Abhishek+Srivastava%2C+and+Iryna+Gurevych%2E+2021%2E+BEIR%3A+A+heterogeneous+benchmark+for+zero%2Dshot+evaluation+of+information+retrieval+models%2E+In+Proceedings+of+)
75. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. [find](https://scholar.google.com/scholar?q=James+Thorne%2C+Andreas+Vlachos%2C+Christos+Christodoulopoulos%2C+and+Arpit+Mittal%2E+2018%2E+FEVER%3A+a+large%2Dscale+dataset+for+fact+extraction+and+VERification%2E+In+Proceedings+of+the+2018+Conference+of+the+Nort) [our card](https://md.research.elamur.ai/free/en/papers/thorne-2018-fever-large-scale-dataset-fact/)
76. Ellen Voorhees. 2005. Overview of the trec 2004 robust retrieval track. In Proceedings of the Thirteenth Text REtrieval Conference. [find](https://scholar.google.com/scholar?q=Ellen+Voorhees%2E+2005%2E+Overview+of+the+trec+2004+robust+retrieval+track%2E+In+Proceedings+of+the+Thirteenth+Text+REtrieval+Conference%2E)
77. Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R. Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. TREC-COVID: Constructing a pandemic information retrieval test collection. SIGIR Forum. [find](https://scholar.google.com/scholar?q=Ellen+Voorhees%2C+Tasmeer+Alam%2C+Steven+Bedrick%2C+Dina+Demner%2DFushman%2C+William+R%2E+Hersh%2C+Kyle+Lo%2C+Kirk+Roberts%2C+Ian+Soboroff%2C+and+Lucy+Lu+Wang%2E+2021%2E+TREC%2DCOVID%3A+Constructing+a+pandemic+information+retrie)
78. Henning Wachsmuth, Shahbaz Syed, and Benno Stein. 2018. Retrieval of the best counterargument without prior topic knowledge. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Henning+Wachsmuth%2C+Shahbaz+Syed%2C+and+Benno+Stein%2E+2018%2E+Retrieval+of+the+best+counterargument+without+prior+topic+knowledge%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computatio) [our card](https://md.research.elamur.ai/free/en/papers/wachsmuth-2018-retrieval-best-counterargument-without-prior/)
79. David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020. Fact or fiction: Verifying scientific claims. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=David+Wadden%2C+Shanchuan+Lin%2C+Kyle+Lo%2C+Lucy+Lu+Wang%2C+Madeleine+van+Zuylen%2C+Arman+Cohan%2C+and+Hannaneh+Hajishirzi%2E+2020%2E+Fact+or+fiction%3A+Verifying+scientific+claims%2E+In+Proceedings+of+the+2020+Conferenc) [our card](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
80. S. Walker, Stephen Robertson, M. Boughanem, G. J. F. Jones, and K. Sparck Jones. 1998. Okapi at trec-6: Automatic adhoc, vlc, routing, filtering and qsdr. In The Sixth Text REtrieval Conference. [find](https://scholar.google.com/scholar?q=S%2E+Walker%2C+Stephen+Robertson%2C+M%2E+Boughanem%2C+G%2E+J%2E+F%2E+Jones%2C+and+K%2E+Sparck+Jones%2E+1998%2E+Okapi+at+trec%2D6%3A+Automatic+adhoc%2C+vlc%2C+routing%2C+filtering+and+qsdr%2E+In+The+Sixth+Text+REtrieval+Conference%2E)
81. Kexin Wang, Nandan Thakur, Nils Reimers, and Iryna Gurevych. 2022a. GPL: Generative pseudo labeling for unsupervised domain adaptation of dense retrieval. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies… [find](https://scholar.google.com/scholar?q=Kexin+Wang%2C+Nandan+Thakur%2C+Nils+Reimers%2C+and+Iryna+Gurevych%2E+2022a%2E+GPL%3A+Generative+pseudo+labeling+for+unsupervised+domain+adaptation+of+dense+retrieval%2E+In+Proceedings+of+the+2022+Conference+of+the+) [our card](https://md.research.elamur.ai/free/en/papers/wang-2022-gpl-generative-pseudo-labeling-unsupervised/)
82. Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al. 2022b. Super-NaturalInstructions: Generalization via declarative instructions on 1600+ nlp tasks. In Proceedings of the 202… [find](https://scholar.google.com/scholar?q=Yizhong+Wang%2C+Swaroop+Mishra%2C+Pegah+Alipoormolabashi%2C+Yeganeh+Kordi%2C+Amirreza+Mirzaei%2C+Anjana+Arunkumar%2C+Arjun+Ashok%2C+Arut+Selvan+Dhanasekaran%2C+Atharva+Naik%2C+David+Stap%2C+et+al%2E+2022b%2E+Super%2DNaturalIns)
83. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022a. Finetuned language models are zero-shot learners. In Proceedings of International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Maarten+Bosma%2C+Vincent+Zhao%2C+Kelvin+Guu%2C+Adams+Wei+Yu%2C+Brian+Lester%2C+Nan+Du%2C+Andrew+M%2E+Dai%2C+and+Quoc+V+Le%2E+2022a%2E+Finetuned+language+models+are+zero%2Dshot+learners%2E+In+Proceedings+of+Interna)
84. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022b. Emergent abilities of large language models. Transactions on M… [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Yi+Tay%2C+Rishi+Bommasani%2C+Colin+Raffel%2C+Barret+Zoph%2C+Sebastian+Borgeaud%2C+Dani+Yogatama%2C+Maarten+Bosma%2C+Denny+Zhou%2C+Donald+Metzler%2C+Ed+H%2E+Chi%2C+Tatsunori+Hashimoto%2C+Oriol+Vinyals%2C+Percy+Liang%2C) [our card](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
85. Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. [find](https://scholar.google.com/scholar?q=Ikuya+Yamada%2C+Akari+Asai%2C+and+Hannaneh+Hajishirzi%2E+2021%2E+Efficient+passage+retrieval+with+hashing+for+open%2Ddomain+question+answering%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+C)
86. Yi Yang, Wen-tau Yih, and Christopher Meek. 2015. WikiQA: A challenge dataset for open-domain question answering. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Yi+Yang%2C+Wen%2Dtau+Yih%2C+and+Christopher+Meek%2E+2015%2E+WikiQA%3A+A+challenge+dataset+for+open%2Ddomain+question+answering%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Language+Process) [our card](https://md.research.elamur.ai/free/en/papers/yang-2015-wikiqa-challenge-dataset-open-domain/)
87. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. [find](https://scholar.google.com/scholar?q=Zhilin+Yang%2C+Peng+Qi%2C+Saizheng+Zhang%2C+Yoshua+Bengio%2C+William+Cohen%2C+Ruslan+Salakhutdinov%2C+and+Christopher+D%2E+Manning%2E+2018%2E+HotpotQA%3A+A+dataset+for+diverse%2C+explainable+multi%2Dhop+question+answering%2E+I) [our card](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
88. Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021. Optimizing dense retrieval model training with hard negatives. In SI-GIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. [find](https://scholar.google.com/scholar?q=Jingtao+Zhan%2C+Jiaxin+Mao%2C+Yiqun+Liu%2C+Jiafeng+Guo%2C+Min+Zhang%2C+and+Shaoping+Ma%2E+2021%2E+Optimizing+dense+retrieval+model+training+with+hard+negatives%2E+In+SI%2DGIR+%E2%80%9921%3A+The+44th+International+ACM+SIGIR+Confe)
89. Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018. ReCoRD: Bridging the gap between human and machine commonsense reading comprehension. [find](https://scholar.google.com/scholar?q=Sheng+Zhang%2C+Xiaodong+Liu%2C+Jingjing+Liu%2C+Jianfeng+Gao%2C+Kevin+Duh%2C+and+Benjamin+Van+Durme%2E+2018%2E+ReCoRD%3A+Bridging+the+gap+between+human+and+machine+commonsense+reading+comprehension%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2018-record-bridging-gap-between-human/)