most citedTrustLLM: Trustworthiness in Large Language Models

54 citations · 64 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024

Why Does New Knowledge Create Messy Ripple Effects in LLMs?

Jiaxin Qin, Zixuan Zhang, Manling Li +2

Extensive previous research has focused on post-training knowledge editing (KE) for language models (LMs) to ensure that knowledge remains accurate and up-to-date. One desired prop…

cs.CL2024★ 1 cited

EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries

Jiateng Liu, Pengfei Yu, Yuji Zhang +3

The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in large language models (LLMs) for knowledge updating. However, current KE approaches, w…

cs.CL2024

Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction

Qingyun Wang, Zixuan Zhang, Hongxiang Li +4

Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemi…

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.HC2023

RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor

Khanh Duy Nguyen, Zixuan Zhang, Reece Suchocki +5

In this paper, we present RESIN-EDITOR, an interactive event graph visualizer and editor designed for analyzing complex events. Our RESIN-EDITOR system allows users to render and f…

cs.CL2023★ 8 cited

Large Language Models on Graphs: A Comprehensive Survey

Bowen Jin, Gang Liu, Chi Han +3

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and n…

cs.CL2023★ 1 cited

TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction

Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh +6

Event extraction has gained considerable interest due to its wide-ranging applications. However, recent studies draw attention to evaluation issues, suggesting that reported scores…