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20232025
most citedSora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

105 citations · 193 across the 15 of their papers we have counts for

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10 papers · 1 filter

cs.CL2024★ 1 cited

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?

Yue Huang, Chenrui Fan, Yuan Li +4

Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they e…

cs.CL2024

DataGen: Unified Synthetic Dataset Generation via Large Language Models

Yue Huang, Siyuan Wu, Chujie Gao +8

Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expen…

cs.CL2024★ 7 cited

Evaluating Large Language Models with Psychometrics

Yuan Li, Yue Huang, Hongyi Wang +4

Large Language Models (LLMs) have demonstrated exceptional capabilities in solving various tasks, progressively evolving into general-purpose assistants. The increasing integration…

cs.CL2024

Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings

Yue Huang, Jingyu Tang, Dongping Chen +5

Recently, Large Language Models (LLMs) have garnered significant attention for their exceptional natural language processing capabilities. However, concerns about their trustworthi…

cs.CL2024

HonestLLM: Toward an Honest and Helpful Large Language Model

Chujie Gao, Siyuan Wu, Yue Huang +6

Large Language Models (LLMs) have achieved remarkable success across various industries due to their exceptional generative capabilities. However, for safe and effective real-world…

cs.CL2024★ 1 cited

I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench

Yuan Li, Yue Huang, Yuli Lin +3

Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this paper, we introduce AwareBench, a benchmark designed to evaluate awareness in LLMs. Drawin…

cs.CL2024★ 2 cited

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?

Qihui Zhang, Chujie Gao, Dongping Chen +8

With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it pote…

cs.CL2024★ 54 cited

TrustLLM: Trustworthiness in Large Language Models

Yue Huang, Lichao Sun, Haoran Wang +67

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…

cs.CL2023★ 1 cited

AlignBench: Benchmarking Chinese Alignment of Large Language Models

Xiao Liu, Xuanyu Lei, Shengyuan Wang +15

Alignment has become a critical step for instruction-tuned Large Language Models (LLMs) to become helpful assistants. However, the effective evaluation of alignment for emerging Ch…

cs.CL2023★ 14 cited

FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models

Yue Huang, Lichao Sun

The rampant spread of fake news has adversely affected society, resulting in extensive research on curbing its spread. As a notable milestone in large language models (LLMs), ChatG…