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Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs).
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
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Bigbench: Towards an industry standard benchmark for big data analytics
Ahmad Ghazal, Tilmann Rabl, Minqing Hu, Francois Raab, Meikel Poess, Alain Crolotte, and Hans-Arno Jacobsen. 2013 · 2013
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022 · 2022
Earlier work this paper cites.
Ddxplus: a new dataset for automatic medical diagnosis
Arsène Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, and Joumana Ghosn. 2022 · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Earlier work this paper cites.
Exploring lottery prompts for pre-trained language models
Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Haitao Zheng, Zhiyuan Liu, and Pengjun Xie. 2023 · 2023
Earlier work this paper cites.
Multifin: A dataset for multilingual financial nlp
Rasmus Kær Jørgensen, Oliver Brandt, Mareike Hartmann, Xiang Dai, C. Igel, and Desmond Elliott. 2023 · 2023
Cited alongside, same era.
Gpt-4 technical report
OpenAI. 2023 · 2023
Cited alongside, same era.
Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2023 · 2023
Cited alongside, same era.
Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al. 2023 · 2023
Cited alongside, same era.
Efficient guided generation for large language models
Brandon T Willard and Rémi Louf. 2023 · 2023
Cited alongside, same era.
Automata-based constraints for language model decoding
Terry Koo, Frederick Liu, and Luheng He. 2024 · 2024
Closest in time.
instructor
Jason Liu. 2024 · 2024
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State of what art? a call for multi-prompt llm evaluation
Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, and Gabriel Stanovsky. 2024 · 2024
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Json mode
OpenAI. 2024 · 2024
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Infobench: Evaluating instruction following ability in large language models
Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, and Dong Yu. 2024 · 2024
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Gemma: Open models based on gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. 2024 · 2024
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Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, Siddhartha Brahma, Sujoy Basu, Yi Luan, Denny Zhou, and Le Hou. 2023 · 2023
Cited alongside, same era.
Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, et al. 2023 · 2023
Cited alongside, same era.
Generate json output with the gemini api
Google Gemini. 2024 · 2024
Cited alongside, same era.
The impact of reasoning step length on large language models
Mingyu Jin, Qinkai Yu, Haiyan Zhao, Wenyue Hua, Yanda Meng, Yongfeng Zhang, Mengnan Du, et al. 2024 · 2024
Cited alongside, same era.
Introducing the next generation of claude
Anthropic Team. 2024a
Cited in the paper.
Introducing meta llama 3: The most capable openly available llm to date
Meta LLaMA Team. 2024b
Cited in the paper.
Closest in time.
Chain-of-thought reasoning without prompting
Xuezhi Wang and Denny Zhou. 2024 · 2024
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Streambench: Towards benchmarking continuous improvement of language agents
Cheng-Kuang Wu, Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen, and Hung yi Lee. 2024 · 2024
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Fofo: A benchmark to evaluate llms’ format-following capability
Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, Ran Xu, Wenpeng Yin, and Caiming Xiong. 2024 · 2024
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