Object-Level Verbalized Confidence Calibration in Vision-Language Models via Semantic Perturbation

Denoising diffusion probabilistic models

Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020

Earlier work this paper cites.

Teaching models to express their uncertainty in words

Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022

Earlier work this paper cites.

Gpt-4 technical report

Original

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023

Earlier work this paper cites.

Qwen-vl: A frontier large vision-language model with versatile abilities

Original

Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023

Earlier work this paper cites.

Sparks of artificial general intelligence: Early experiments with gpt-4

Original

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023

Earlier work this paper cites.

Discovering latent knowledge in language models without supervision

Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2023 · 2023

Earlier work this paper cites.

Mme: A comprehensive evaluation benchmark for multimodal large language models

Original

Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, et al. 2023 · 2023

Earlier work this paper cites.

A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023 · 2023

Earlier work this paper cites.

Segment anything

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. 2023 · 2023

Earlier work this paper cites.

Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023

Cited alongside, same era.

Conformal prediction with large language models for multi-choice question answering

Original

Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, and Andrew Beam. 2023 · 2023

Cited alongside, same era.

Evaluating object hallucination in large vision-language models

Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023

Cited alongside, same era.

An llm-free multi-dimensional benchmark for mllms hallucination evaluation

Original

Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Ming Yan, Ji Zhang, and Jitao Sang. 2023 · 2023

Cited alongside, same era.

Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms

A survey of confidence estimation and calibration in large language models

Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, and Iryna Gurevych. 2024 · 2024

Later among the works it cites.

Overconfidence is key: Verbalized uncertainty evaluation in large language and vision-language models

Tobias Groot and Matias Valdenegro-Toro. 2024 · 2024

Later among the works it cites.

Consistency and uncertainty: Identifying unreliable responses from black-box vision-language models for selective visual question answering

Zaid Khan and Yun Fu. 2024 · 2024

Later among the works it cites.

Confidence under the hood: An investigation into the confidence-probability alignment in large language models

Abhishek Kumar, Robert Morabito, Sanzhar Umbet, Jad Kabbara, and Ali Emami. 2024 · 2024

Later among the works it cites.

Simpo: Simple preference optimization with a reference-free reward

Yu Meng, Mengzhou Xia, and Danqi Chen. 2024 · 2024

Later among the works it cites.