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20232026
most citedTrustLLM: Trustworthiness in Large Language Models

54 citations · 70 across the 12 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.CL2025

Towards Effective Model Editing for LLM Personalization

Baixiang Huang, Limeng Cui, Jiapeng Liu +7

Personalization is becoming indispensable for LLMs to align with individual user preferences and needs. Yet current approaches are often computationally expensive, data-intensive,…

cs.CL2025★ 2 cited

Privacy-Aware Decoding: Mitigating Privacy Leakage of Large Language Models in Retrieval-Augmented Generation

Haoran Wang, Xiongxiao Xu, Baixiang Huang +1

Retrieval-Augmented Generation (RAG) enhances the factual accuracy of large language models (LLMs) by conditioning outputs on external knowledge sources. However, when retrieval in…

cs.CL2025

Can Multimodal LLMs Perform Time Series Anomaly Detection?

Xiongxiao Xu, Haoran Wang, Yueqing Liang +3

Time series anomaly detection (TSAD) has been a long-standing pillar problem in Web-scale systems and online infrastructures, such as service reliability monitoring, system fault d…

cs.CY2025★ 1 cited

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.…

cs.CL2025

Benchmarking LLMs for Political Science: A United Nations Perspective

Yueqing Liang, Liangwei Yang, Chen Wang +6

Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplo…