Fetching the paper…
Reading the bibliography…
Large Language Models (LLMs) have demonstrated remarkable self-improvement capabilities, whereby models iteratively revise their outputs through self-generated feedback.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Shane Storks, Qiaozi Gao, and Joyce Y Chai · 2019
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
Calibration tests beyond classification
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2021
Earlier work this paper cites.
Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 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 Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.
A survey of joint intent detection and slot filling models in natural language understanding
Henry Weld, Xiaoqi Huang, Siqu Long, Josiah Poon, and Soyeon Caren Han · 2022
Earlier work this paper cites.
Rl4f: Generating natural language feedback with reinforcement learning for repairing model outputs
Afra Feyza Akyürek, Ekin Akyürek, Ashwin Kalyan, Peter Clark, Derry Tanti Wijaya, and Niket Tandon · 2023
Earlier work this paper cites.
The internal state of an llm knows when it’s lying
Amos Azaria and Tom Mitchell · 2023
Earlier work this paper cites.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
Earlier work this paper cites.
Baldur: Whole-proof generation and repair with large language models
Emily First, Markus N Rabe, Talia Ringer, and Yuriy Brun · 2023
Earlier work this paper cites.
A survey of confidence estimation and calibration in large language models
Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, and Iryna Gurevych · 2023
Earlier work this paper cites.
Critic: Large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Nan Duan, and Weizhu Chen · 2023
Earlier work this paper cites.
Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
Earlier work this paper cites.
Inference-time intervention: Eliciting truthful answers from a language model
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg · 2023
Earlier work this paper cites.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
Cited alongside, same era.
Is self-repair a silver bullet for code generation?
Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama · 2023
Cited alongside, same era.
Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
Cited alongside, same era.
Llamas know what gpts don’t show: Surrogate models for confidence estimation
Vaishnavi Shrivastava, Percy Liang, and Ananya Kumar · 2023
Cited alongside, same era.
Large language models in medicine
Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting · 2023
Cited alongside, same era.
Is temperature the creativity parameter of large language models?
Max Peeperkorn, Tom Kouwenhoven, Dan Brown, and Anna Jordanous · 2024
Later among the works it cites.
Thermometer: Towards universal calibration for large language models
Maohao Shen, Subhro Das, Kristjan Greenewald, Prasanna Sattigeri, Gregory W Wornell, and Soumya Ghosh · 2024
Later among the works it cites.
Api is enough: Conformal prediction for large language models without logit-access
Jiayuan Su, Jing Luo, Hongwei Wang, and Lu Cheng · 2024
Later among the works it cites.
Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, et al · 2024
Later among the works it cites.
Large language models for data annotation and synthesis: A survey
Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, and Huan Liu · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
Cited alongside, same era.
Verify-and-edit: A knowledge-enhanced chain-of-thought framework
Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing · 2023
Cited alongside, same era.
Learning from natural language feedback
A Chen, J Scheurer, JA Campos, T Korbak, JS Chan, SR Bowman, K Cho, and E Perez · 2024
Cited alongside, same era.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason E Weston · 2024
Cited alongside, same era.
Small language model can self-correct
Haixia Han, Jiaqing Liang, Jie Shi, Qianyu He, and Yanghua Xiao · 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
Cited alongside, same era.
Calibrated decision-making through llm-assisted retrieval
Chaeyun Jang, Hyungi Lee, Seanie Lee, and Juho Lee · 2024
Cited alongside, same era.
Later among the works it cites.
Can llms learn from previous mistakes? investigating llms’ errors to boost for reasoning
Yongqi Tong, Dawei Li, Sizhe Wang, Yujia Wang, Fei Teng, and Jingbo Shang · 2024
Later among the works it cites.
Self-preference bias in llm-as-a-judge
Koki Wataoka, Tsubasa Takahashi, and Ryokan Ri · 2024
Later among the works it cites.
Progress or regress? self-improvement reversal in post-training
Ting Wu, Xuefeng Li, and Pengfei Liu · 2024
Later among the works it cites.
Calibrating language models with adaptive temperature scaling
Johnathan Xie, Annie Chen, Yoonho Lee, Eric Mitchell, and Chelsea Finn · 2024
Later among the works it cites.
Sayself: Teaching llms to express confidence with self-reflective rationales
Tianyang Xu, Shujin Wu, Shizhe Diao, Xiaoze Liu, Xingyao Wang, Yangyi Chen, and Jing Gao · 2024
Later among the works it cites.
Tairan Fu, Javier Conde, Gonzalo Martínez, María Grandury, and Pedro Reviriego · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
Closest in time.
On the trustworthiness of generative foundation models: Guideline, assessment, and perspective
Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, et al · 2025
Closest in time.
Teaching language models to critique via reinforcement learning
Zhihui Xie, Liyu Chen, Weichao Mao, Jingjing Xu, Lingpeng Kong, et al · 2025
Closest in time.
Deepreview: Improving llm-based paper review with human-like deep thinking process
Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang · 2025
Closest in time.