4 citations · 13 across the 10 of their papers we have counts for
10 papers
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5
Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors
Tianchun Wang, Yuanzhou Chen, Zichuan Liu +4
The advent of large language models (LLMs) has revolutionized the field of text generation, producing outputs that closely mimic human-like writing. Although academic and industria…
Through the Theory of Mind's Eye: Reading Minds with Multimodal Video Large Language Models
Zhawnen Chen, Tianchun Wang, Yizhou Wang +4
Can large multimodal models have a human-like ability for emotional and social reasoning, and if so, how does it work? Recent research has discovered emergent theory-of-mind (ToM)…
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Zichuan Liu, Tianchun Wang, Jimeng Shi +7
Explaining deep learning models operating on time series data is crucial in various applications of interest which require interpretable and transparent insights from time series s…
Protecting Your LLMs with Information Bottleneck
Zichuan Liu, Zefan Wang, Linjie Xu +6
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to et…
Parametric Augmentation for Time Series Contrastive Learning
Xu Zheng, Tianchun Wang, Wei Cheng +4
Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating po…
PAC Learnability under Explanation-Preserving Graph Perturbations
Xu Zheng, Farhad Shirani, Tianchun Wang +4
Graphical models capture relations between entities in a wide range of applications including social networks, biology, and natural language processing, among others. Graph neural…
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Zichuan Liu, Yingying Zhang, Tianchun Wang +8
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns. Although prev…
DyExplainer: Explainable Dynamic Graph Neural Networks
Tianchun Wang, Dongsheng Luo, Wei Cheng +2
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
Xu Zheng, Farhad Shirani, Tianchun Wang +5
Graph Neural Networks (GNNs) are neural models that leverage the dependency structure in graphical data via message passing among the graph nodes. GNNs have emerged as pivotal arch…