Built on

Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Gordon V. Cormack, Charles L. A. Clarke, and Stefan Buettcher. 2009 · 2009
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Christopher J.C. Burges. 2010 · 2010
Earlier work this paper cites.
Improvements to bm25 and language models examined
Andrew Trotman, Antti Puurula, and Blake Burgess. 2014 · 2014
Earlier work this paper cites.
Realm: Retrieval-augmented language model pre-training
Original
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Original
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
Large dual encoders are generalizable retrievers
Original
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. 2021 · 2021
Earlier work this paper cites.

Similar

Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Original
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Original
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Lost in the middle: How language models use long contexts
Original
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
Cited alongside, same era.
Chameleon: Plug-and-play compositional reasoning with large language models
Original
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023 · 2023
Cited alongside, same era.

Then

Large language models are effective text rankers with pairwise ranking prompting
Original
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
Closest in time.
In-context retrieval-augmented language models
Original
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Original
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
Closest in time.
Quill: Query intent with large language models using retrieval augmentation and multi-stage distillation
Original
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, and Mike Bendersky. 2023 · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2023

Context Tuning for Retrieval Augmented Generation

Anantha, Raviteja, Bethi, Tharun, Vodianik, Danil et al.

Understand

Large language models (LLMs) have the remarkable ability to solve new tasks with just a few examples, but they need access to the right tools.

Retrieval Augmented Generation (RAG) addresses this problem by retrieving a list of relevant tools for a given task.
However, RAG's tool retrieval step requires all the required information to be explicitly present in the query.
This is a limitation, as semantic search, the widely adopted tool retrieval method, can fail when the query is incomplete or lacks context.

Built on

Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Gordon V. Cormack, Charles L. A. Clarke, and Stefan Buettcher. 2009 · 2009
Earlier work this paper cites.
From ranknet to lambdarank to lambdamart: An overview
Christopher J.C. Burges. 2010 · 2010
Earlier work this paper cites.
Improvements to bm25 and language models examined
Andrew Trotman, Antti Puurula, and Blake Burgess. 2014 · 2014
Earlier work this paper cites.
Realm: Retrieval-augmented language model pre-training
Original
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive nlp tasks
Original
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
Large dual encoders are generalizable retrievers
Original
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. 2021 · 2021
Earlier work this paper cites.

Similar

Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Original
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Original
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Lost in the middle: How language models use long contexts
Original
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
Cited alongside, same era.
Chameleon: Plug-and-play compositional reasoning with large language models
Original
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. 2023 · 2023
Cited alongside, same era.

Then

Large language models are effective text rankers with pairwise ranking prompting
Original
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
Closest in time.
In-context retrieval-augmented language models
Original
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Original
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
Closest in time.
Quill: Query intent with large language models using retrieval augmentation and multi-stage distillation
Original
Krishna Srinivasan, Karthik Raman, Anupam Samanta, Lingrui Liao, Luca Bertelli, and Mike Bendersky. 2023 · 2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Query rewriting for retrieval-augmented large language models

Original

Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023

Cited alongside, same era.

Gpt-4 technical report

Original

OpenAI. 2023 · 2023

Cited alongside, same era.

Llama: Open and efficient foundation language models

Original

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023

Closest in time.