105 citations · 306 across the 26 of their papers we have counts for
29 papers
MiniRep: Robust Reputation-Based Aggregation for Multi-Agent Debate
Jiaming Zhang, Yuwan Liu, Yue Huang +1
Autonomous agents powered by large language models (LLMs) are rapidly evolving into an open agentic ecosystem. To support trustworthy collaboration, industry initiatives increasing…
MADBench: Benchmarking the Security of Multi-Agent Debate
Yuwan Liu, Jiaming Zhang, Yue Huang +1
Multi-agent debate (MAD) can improve large language model (LLM) reasoning by allowing multiple agents to exchange and critique their answers to the same task. However, the interact…
RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty
Ziqian Zhang, Xingjian Hu, Yue Huang +8
Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving ad…
Dep-Search: Learning Dependency-Aware Reasoning Traces with Persistent Memory
Yanming Liu, Xinyue Peng, Zixuan Yan +7
Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly when augmented with search mechanisms that enable systematic explora…
Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models
Rong Zhou, Dongping Chen, Zihan Jia +24
Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration o…
TASE: Token Awareness and Structured Evaluation for Multilingual Language Models
Chenzhuo Zhao, Xinda Wang, Yue Huang +2
While large language models (LLMs) have demonstrated remarkable performance on high-level semantic tasks, they often struggle with fine-grained, token-level understanding and struc…
AsFT: Anchoring Safety During LLM Fine-Tuning Within Narrow Safety Basin
Shuo Yang, Qihui Zhang, Yuyang Liu +7
Fine-tuning large language models (LLMs) improves performance but introduces critical safety vulnerabilities: even minimal harmful data can severely compromise safety measures. We…
EfficientLLM: Efficiency in Large Language Models
Zhengqing Yuan, Weixiang Sun, Yixin Liu +13
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We intr…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
ReadMe.LLM: A Framework to Help LLMs Understand Your Library
Sandya Wijaya, Jacob Bolano, Alejandro Gomez Soteres +3
Large Language Models (LLMs) often struggle with code generation tasks involving niche software libraries. Existing code generation techniques with only human-oriented documentatio…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment
Dongping Chen, Ruoxi Chen, Shu Pu +8
Many real-world user queries (e.g. "How do to make egg fried rice?") could benefit from systems capable of generating responses with both textual steps with accompanying images, si…
Can Large Language Models Automatically Jailbreak GPT-4V?
Yuanwei Wu, Yue Huang, Yixin Liu +3
GPT-4V has attracted considerable attention due to its extraordinary capacity for integrating and processing multimodal information. At the same time, its ability of face recogniti…
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…
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…
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…
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…
GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding
Dongping Chen, Yue Huang, Siyuan Wu +17
Recently, Multimodal Large Language Models (MLLMs) have been used as agents to control keyboard and mouse inputs by directly perceiving the Graphical User Interface (GUI) and gener…
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…
Optimization-based Prompt Injection Attack to LLM-as-a-Judge
Jiawen Shi, Zenghui Yuan, Yinuo Liu +4
LLM-as-a-Judge uses a large language model (LLM) to select the best response from a set of candidates for a given question. LLM-as-a-Judge has many applications such as LLM-powered…
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…
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Yixin Liu, Kai Zhang, Yuan Li +9
Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions…
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…
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…