no code implementations • 7 Feb 2024 • Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Fan Yang, Mengnan Du, Xuanting Cai, Xia Hu
In this work, we introduce a generative explanation framework, xLLM, to improve the faithfulness of the explanations provided in natural language formats for LLMs.
no code implementations • 23 Dec 2023 • Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai, Xia Hu
To address this problem, we develop a pre-trained, DNN-based, generic explainer on large-scale image datasets, and leverage its transferability to explain various vision models for downstream tasks.
1 code implementation • 5 Mar 2023 • Yu-Neng Chuang, Guanchu Wang, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu
In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.
no code implementations • 7 Feb 2023 • Yu-Neng Chuang, Guanchu Wang, Fan Yang, Zirui Liu, Xuanting Cai, Mengnan Du, Xia Hu
Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 17 Jun 2022 • Guanchu Wang, Yu-Neng Chuang, Mengnan Du, Fan Yang, Quan Zhou, Pushkar Tripathi, Xuanting Cai, Xia Hu
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
3 code implementations • 14 Feb 2022 • Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang, Yi-Wei Chen, Daochen Zha, Alfredo Costilla Reyes, Afshin Niktash, Gorkem Ulkar, Erman Okman, Xuanting Cai, Xia Hu
DNNs have been an effective tool for data processing and analysis.
no code implementations • Findings (EMNLP) 2021 • Xuanting Cai, Quanbin Ma, Pan Li, Jianyu Liu, Qi Zeng, Zhengkan Yang, Pushkar Tripathi
Understanding the semantic meaning of content on the web through the lens of entities and concepts has many practical advantages.
no code implementations • 6 Mar 2021 • Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Vasileios Maroulas, Xuanting Cai
In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved.
no code implementations • 25 Feb 2021 • Mustafa Hajij, Ghada Zamzmi, Xuanting Cai
This article aims to study the topological invariant properties encoded in node graph representational embeddings by utilizing tools available in persistent homology.