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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Original
Nils Reimers and Iryna Gurevych. 2019 · 1908
Earlier work this paper cites.
Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin. 2018 · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Original
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Original
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2018 · 2018
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Original
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018
Earlier work this paper cites.
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Earlier work this paper cites.

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Picard: Parsing incrementally for constrained auto-regressive decoding from language models
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Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
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Empowering language models with knowledge graph reasoning for question answering
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Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, and Yizhou Sun. 2022 · 2022
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Few-shot learning with retrieval augmented language models
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Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
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Talm: Tool augmented language models
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Aaron Parisi, Yao Zhao, and Noah Fiedel. 2022 · 2022
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Then

Chatdb: Augmenting llms with databases as their symbolic memory
Original
Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, and Hang Zhao. 2023 · 2023
Later among the works it cites.
Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023 · 2023
Later among the works it cites.
Gpt-4 technical report
Original
OpenAI. 2023 · 2023
Later among the works it cites.
Din-sql: Decomposed in-context learning of text-to-sql with self-correction
Original
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Later among the works it cites.
In-context retrieval-augmented language models
Original

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2024

Relational Database Augmented Large Language Model

Qin, Zongyue, Luo, Chen, Wang, Zhengyang et al.

Understand

Large language models (LLMs) excel in many natural language processing (NLP) tasks.

However, since LLMs can only incorporate new knowledge through training or supervised fine-tuning processes, they are unsuitable for applications that demand precise, up-to-date, and private information not available in the training corpora.
This precise, up-to-date, and private information is typically stored in relational databases.
Thus, a promising solution is to augment LLMs with the inclusion of relational databases as external memory.

Built on

Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Original
Nils Reimers and Iryna Gurevych. 2019 · 1908
Earlier work this paper cites.
Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin. 2018 · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Original
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Original
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2018 · 2018
Earlier work this paper cites.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Original
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018
Earlier work this paper cites.
Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Earlier work this paper cites.

Similar

Picard: Parsing incrementally for constrained auto-regressive decoding from language models
Original
Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
Cited alongside, same era.
Empowering language models with knowledge graph reasoning for question answering
Original
Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang, Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, and Yizhou Sun. 2022 · 2022
Cited alongside, same era.
Few-shot learning with retrieval augmented language models
Original
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
Cited alongside, same era.
Talm: Tool augmented language models
Original
Aaron Parisi, Yao Zhao, and Noah Fiedel. 2022 · 2022
Cited alongside, same era.

Then

Chatdb: Augmenting llms with databases as their symbolic memory
Original
Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, and Hang Zhao. 2023 · 2023
Later among the works it cites.
Chatgpt: Jack of all trades, master of none
Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, et al. 2023 · 2023
Later among the works it cites.
Gpt-4 technical report
Original
OpenAI. 2023 · 2023
Later among the works it cites.
Din-sql: Decomposed in-context learning of text-to-sql with self-correction
Original
Mohammadreza Pourreza and Davood Rafiei. 2023 · 2023
Later among the works it cites.
In-context retrieval-augmented language models
Original

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Importance of synthesizing high-quality data for text-to-sql parsing

Original

Yiyun Zhao, Jiarong Jiang, Yiqun Hu, Wuwei Lan, Henry Zhu, Anuj Chauhan, Alexander Li, Lin Pan, Jun Wang, Chung-Wei Hang, et al. 2022 · 2022

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Claude-2, https://www.anthropic.com/index/claude-2

Anthropic. 2023 · 2023

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Dr. spider: A diagnostic evaluation benchmark towards text-to-sql robustness

Original

Shuaichen Chang, Jun Wang, Mingwen Dong, Lin Pan, Henghui Zhu, Alexander Hanbo Li, Wuwei Lan, Sheng Zhang, Jiarong Jiang, Joseph Lilien, et al. 2023 · 2023

Cited alongside, same era.

C3: Zero-shot text-to-sql with chatgpt

Original

Xuemei Dong, Chao Zhang, Yuhang Ge, Yuren Mao, Yunjun Gao, Jinshu Lin, Dongfang Lou, et al. 2023 · 2023

Cited alongside, same era.

Resdsql: Decoupling schema linking and skeleton parsing for text-to-sql

Haoyang Li, Jing Zhang, Cuiping Li, and Hong Chen. 2023a

Cited in the paper.

Api-bank: A benchmark for tool-augmented llms

Original

Minghao Li, Feifan Song, Bowen Yu, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. 2023b

Cited in the paper.

Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023

Later among the works it cites.

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

Later among the works it cites.

Replug: Retrieval-augmented black-box language models

Original

Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2023 · 2023

Later among the works it cites.