718 citations · 762 across the 8 of their papers we have counts for
9 papers
Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
Yiyou Sun, Yu Gai, Lijie Chen +3
Large language models (LLMs) frequently generate hallucinations-content that deviates from factual accuracy or provided context-posing challenges for diagnosis due to the complex i…
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen +22
Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have re…
KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking
Jiawei Zhang, Chejian Xu, Yu Gai +3
This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation qu…
Blockchain Large Language Models
Yu Gai, Liyi Zhou, Kaihua Qin +2
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activ…
Grounded Graph Decoding Improves Compositional Generalization in Question Answering
Yu Gai, Paras Jain, Wendi Zhang +3
Question answering models struggle to generalize to novel compositions of training patterns, such to longer sequences or more complex test structures. Current end-to-end models lea…
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Yatong Bai, Tanmay Gautam, Yu Gai +1
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program. Unfortu…
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Jianfei Chen, Yu Gai, Zhewei Yao +2
Fully quantized training (FQT), which uses low-bitwidth hardware by quantizing the activations, weights, and gradients of a neural network model, is a promising approach to acceler…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…
Loss Functions for Multiset Prediction
Sean Welleck, Zixin Yao, Yu Gai +3
We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing…