activity
20182026
most citedTrustLLM: Trustworthiness in Large Language Models

54 citations · 77 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models

Jiaxin Zhang, Wendi Cui, Zhuohang Li +4

While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functiona…

cs.CL2025

VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents

Sam Yu-Te Lee, Chenyang Ji, Shicheng Wen +3

Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrier for entry-level analysts. Recent adv…

cs.CL2025★ 2 cited

RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models

Bang An, Shiyue Zhang, Mark Dredze

Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…

cs.CL2024

DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models

Yiming Huang, Jianwen Luo, Yan Yu +8

We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks. This benchmark features three core elements: First, the ta…

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.CL2019★ 8 cited

Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Lifu Huang, Ronan Le Bras, Chandra Bhagavatula +1

Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicit…

cs.CL2018★ 13 cited

Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension

Bhavana Dalvi Mishra, Lifu Huang, Niket Tandon +2

We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, Pro…