Fetching the paper…
Reading the bibliography…
Calibration is crucial as large language models (LLMs) are increasingly deployed to convert natural language queries into SQL for commercial databases.
Verification of Forecasts Expressed in Terms of Probability
Glenn W. Brier. 1950 · 1950
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt. 2000 · 2000
Earlier work this paper cites.
Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan. 2002 · 2002
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana. 2005 · 2005
Earlier work this paper cites.
Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv. 2017 · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017a · 2017
Earlier work this paper cites.
Calibration of encoder decoder models for neural machine translation
Aviral Kumar and Sunita Sarawagi. 2019 · 2019
Earlier work this paper cites.
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2019 · 2019
Earlier work this paper cites.
Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
Earlier work this paper cites.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. 2022 · 2022
Earlier work this paper cites.
Uncertainty in natural language generation: From theory to applications
Joris Baan, Nico Daheim, Evgenia Ilia, Dennis Ulmer, Haau-Sing Li, R. Fernández, Barbara Plank, Rico Sennrich, Chrysoula Zerva, and Wilker Aziz. 2023 · 2023
Earlier work this paper cites.
Benchmarking and improving text-to-SQL generation under ambiguity
Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe, and Sunita Sarawagi. 2023 · 2023
Cited alongside, same era.
Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation
Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
Cited alongside, same era.
Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C. C. Chang, Fei Huang, Reynold Cheng, and Yongbin Li. 2023 · 2023
Cited alongside, same era.
Tce: A test-based approach to measuring calibration error
Takuo Matsubara, Niek Tax, Richard Mudd, and Ido Guy. 2023 · 2023
Cited alongside, same era.
Self-evaluation improves selective generation in large language models
Large language models must be taught to know what they don’t know
Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, and Andrew Gordon Wilson. 2024 · 2024
Closest in time.
Codes: Towards building open-source language models for text-to-sql
Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li, and Hong Chen. 2024 · 2024
Closest in time.
Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2024 · 2024
Closest in time.
Zero and few-shot semantic parsing with ambiguous inputs
Elias Stengel-Eskin, Kyle Rawlins, and Benjamin Van Durme. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jie Ren, Yao Zhao, Tu Vu, Peter J. Liu, and Balaji Lakshminarayanan. 2023 · 2023
Cited alongside, same era.
Calibrated interpretation: Confidence estimation in semantic parsing
Elias Stengel-Eskin and Benjamin Van Durme. 2023 · 2023
Cited alongside, same era.
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher Manning. 2023 · 2023
Cited alongside, same era.
Navigating the grey area: How expressions of uncertainty and overconfidence affect language models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto. 2023 · 2023
Cited alongside, same era.
Introducing llama 3.1: Our most capable models to date
Meta AI. 2024 · 2024
Cited alongside, same era.
OpenAI Employees. 2024 · 2024
Cited alongside, same era.
Calibrating long-form generations from large language models
Yukun Huang, Yixin Liu, Raghuveer Thirukovalluru, Arman Cohan, and Bhuwan Dhingra. 2024 · 2024
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017b
Cited in the paper.
The calibration gap between model and human confidence in large language models
Mark Steyvers, Heliodoro Tejeda Lemus, Aakriti Kumar, Catarina Belem, Sheer Karny, Xinyue Hu, Lukas Mayer, and Padhraic Smyth. 2024 · 2024
Closest in time.
Mac-sql: A multi-agent collaborative framework for text-to-sql
Bing Wang, Changyu Ren, Jian Yang, Xinnian Liang, Jiaqi Bai, Linzheng Chai, Zhao Yan, Qian-Wen Zhang, Di Yin, Xing Sun, and Zhoujun Li. 2024 · 2024
Closest in time.
The generative AI paradox: “what it can create, it may not understand”
Peter West, Ximing Lu, Nouha Dziri, Faeze Brahman, Linjie Li, Jena D. Hwang, Liwei Jiang, Jillian Fisher, Abhilasha Ravichander, Khyathi Chandu, Benjamin Newman, Pang Wei Koh, Allyson Ettinger, and Yejin Choi. 2024 · 2024
Closest in time.
Cheng-Kuang Wu, Zhi Rui Tam, Chao-Chung Wu, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen. 2024 · 2024
Closest in time.
Calibrating language models with adaptive temperature scaling
Johnathan Xie, Annie S. Chen, Yoonho Lee, Eric Mitchell, and Chelsea Finn. 2024 · 2024
Closest in time.
Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu, YIFEI LI, Jie Fu, Junxian He, and Bryan Hooi. 2024 · 2024
Closest in time.