Fetching the paper…
Reading the bibliography…
In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-readable narratives.
Sus: a “quick and dirty’usability
John Brooke. 1996 · 1996
Earlier work this paper cites.
Using data mining to predict secondary school student performance
Paulo Cortez and Alice Silva. 2008 · 2008
Earlier work this paper cites.
Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project
Dean De Cock. 2011 · 2011
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Earlier work this paper cites.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, and others. 2020 · 2020
Earlier work this paper cites.
Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020 · 2020
Earlier work this paper cites.
Helen Jiang and Erwen Senge. 2021 · 2021
Earlier work this paper cites.
Considerations for Deploying xAI Tools in the Wild: Lessons Learned from xAI Deployment in a Cybersecurity Operations Setting.. In Proposed for presentation at the ACM SIG Knowledge Discovery and Data Mining Workshop on Responsible AI held August 14-18, 2021 in Singapore, Singapore
Megan Nyre-Yu, Elizabeth Morris, Blake Moss, Charles Smutz, and Michael Smith. 2021 · 2021
Earlier work this paper cites.
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
Jianlong Zhou, Amir H. Gandomi, Fang Chen, and Andreas Holzinger. 2021 · 2021
Cited alongside, same era.
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, and Kalyan Veeramachaneni. 2021 · 2021
Cited alongside, same era.
Interpretation Quality Score for Measuring the Quality of Interpretability Methods
Yuansheng Xie, Soroush Vosoughi, and Saeed Hassanpour. 2022 · 2022
Cited alongside, same era.
Natural Language Explanations for Machine Learning Classification Decisions. In 2023 International Joint Conference on Neural Networks (IJCNN)
James Burton, Noura Al Moubayed, and Amir Enshaei. 2023 · 2023
Cited alongside, same era.
A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, and others. 2023 · 2023
Are Large Language Models Post Hoc Explainers?
Nicholas Kroeger, Dan Ley, Satyapriya Krishna, Chirag Agarwal, and Himabindu Lakkaraju. 2023 · 2023
Later among the works it cites.
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent. In World Conference on Explainable Artificial Intelligence
Van Bach Nguyen, Jörg Schlötterer, and Christin Seifert. 2023 · 2023
Later among the works it cites.
ConvXAI : Delivering Heterogeneous AI Explanations via Conversations to Support Human-AI Scientific Writing
Hua Shen, Chieh-Yang Huang, Tongshuang Wu, and Ting-Hao Kenneth Huang. 2023 · 2023
Later among the works it cites.
Explaining machine learning models with interactive natural language conversations using TalkToModel
Dylan Slack, Satyapriya Krishna, Himabindu Lakkaraju, and Sameer Singh. 2023 · 2023
Later among the works it cites.
Survey on explainable AI: From approaches, limitations and Applications aspects
Wenli Yang, Yuchen Wei, Hanyu Wei, Yanyu Chen, Guan Huang, Xiang Li, Renjie Li, Naimeng Yao, Xinyi Wang, Xiaotong Gu, and others. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Adapting Prompt for Few-shot Table-to-Text Generation
Zhixin Guo, Minyxuan Yan, Jiexing Qi, Jianping Zhou, Ziwei He, Zhouhan Lin, Guanjie Zheng, and Xinbing Wang. 2023 · 2023
Cited alongside, same era.
Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance
Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman. 2023 · 2023
Cited alongside, same era.
Later among the works it cites.
Towards LLM-guided Causal Explainability for Black-box Text Classifiers
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu. 2024 · 2024
Closest in time.
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi, Fan Yang, Tianming Liu, Xiaoming Zhai, Wenlin Yao, Jundong Li, Mengnan Du, and Ninghao Liu. 2024 · 2024
Closest in time.