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20232026
most citedA Survey on Detection of LLMs-Generated Content

7 citations · 36 across the 24 of their papers we have counts for

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7 papers · 1 filter

cs.LG2023★ 4 cited

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…

cs.CL2023★ 7 cited

A Survey on Detection of LLMs-Generated Content

Xianjun Yang, Liangming Pan, Xuandong Zhao +4

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of s…

cs.CL2023★ 3 cited

Zero-Shot Detection of Machine-Generated Codes

Xianjun Yang, Kexun Zhang, Haifeng Chen +3

This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…

cs.CL2023

Distantly-Supervised Joint Extraction with Noise-Robust Learning

Yufei Li, Xiao Yu, Yanghong Guo +3

Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-la…

cs.CL2023★ 3 cited

PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners

Yijia Xiao, Yiqiao Jin, Yushi Bai +10

The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless…

cs.LG2023★ 4 cited

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…

cs.LG2023★ 3 cited

Interpretable Imitation Learning with Dynamic Causal Relations

Tianxiang Zhao, Wenchao Yu, Suhang Wang +6

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving…