Can Explanations Be Useful for Calibrating Black Box Models?

14 October 2021

Papers citing "Can Explanations Be Useful for Calibrating Black Box Models?"

21 / 21 papers shown

Title
A Survey of Calibration Process for Black-Box LLMs Liangru Xie Hui Liu Jingying Zeng Xianfeng Tang Yan Han Chen Luo Jing Huang Zhen Li Suhang Wang Qi He 74 1 0 17 Dec 2024
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks Junlin Hou Sicen Liu Yequan Bie Hongmei Wang Andong Tan Luyang Luo Hao Chen XAI 25 3 0 03 Oct 2024
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach Linyu Liu Yu Pan Xiaocheng Li Guanting Chen 30 24 0 24 Apr 2024
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training Dongfang Li Baotian Hu Qingcai Chen Shan He 26 4 0 29 Dec 2023
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling Bairu Hou Yujian Liu Kaizhi Qian Jacob Andreas Shiyu Chang Yang Zhang UD UQCV PER 23 48 0 15 Nov 2023
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration Rachneet Sachdeva Martin Tutek Iryna Gurevych OODD 22 10 0 14 Sep 2023
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations Yanda Chen Ruiqi Zhong Narutatsu Ri Chen Zhao He He Jacob Steinhardt Zhou Yu Kathleen McKeown LRM 26 47 0 17 Jul 2023
Efficient Shapley Values Estimation by Amortization for Text Classification Chenghao Yang Fan Yin He He Kai-Wei Chang Xiaofei Ma Bing Xiang FAtt VLM 15 4 0 31 May 2023
Getting MoRE out of Mixture of Language Model Reasoning Experts Chenglei Si Weijia Shi Chen Zhao Luke Zettlemoyer Jordan L. Boyd-Graber LRM 24 24 0 24 May 2023
Streamlining models with explanations in the learning loop Francesco Lomuscio P. Bajardi Alan Perotti E. Amparore FAtt 21 0 0 15 Feb 2023
Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Models Albert Xu Xiang Ren Robin Jia OODD 22 2 0 28 Nov 2022
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates Dongfang Li Baotian Hu Qingcai Chen 11 8 0 06 Nov 2022
A Close Look into the Calibration of Pre-trained Language Models Yangyi Chen Lifan Yuan Ganqu Cui Zhiyuan Liu Heng Ji 25 43 0 31 Oct 2022
Prompting GPT-3 To Be Reliable Chenglei Si Zhe Gan Zhengyuan Yang Shuohang Wang Jianfeng Wang Jordan L. Boyd-Graber Lijuan Wang KELM LRM 42 278 0 17 Oct 2022
Assessing Out-of-Domain Language Model Performance from Few Examples Prasann Singhal Jarad Forristal Xi Ye Greg Durrett LRM 20 5 0 13 Oct 2022
ER-Test: Evaluating Explanation Regularization Methods for Language Models Brihi Joshi Aaron Chan Ziyi Liu Shaoliang Nie Maziar Sanjabi Hamed Firooz Xiang Ren AAML 30 6 0 25 May 2022
Re-Examining Calibration: The Case of Question Answering Chenglei Si Chen Zhao Sewon Min Jordan L. Boyd-Graber 59 30 0 25 May 2022
The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning Xi Ye Greg Durrett ReLM LRM 28 168 0 06 May 2022
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 243 289 0 17 Mar 2020
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,956 0 20 Apr 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017