21 citations · 53 across the 15 of their papers we have counts for
15 papers
PRISM Edit: One Vector for All Temporal Answers
Chen Huang, Qi Zheng, Ruiqin Zheng +2
Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not…
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection
Siyuan Cheng, Bozhong Tian, YanChao Hao +1
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei Published: 06 Apr 2026, Last Modified: 06 Apr 2026…
ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
Kangwei Liu, Siyuan Cheng, Bozhong Tian +7
Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the ov…
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
Chenxi Wang, Jizhan Fang, Xiang Chen +4
Recent advancements in Large Multimodal Models (LMMs) have shown promise in Autonomous Driving Systems (ADS). However, their direct application to ADS is hindered by challenges suc…
MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation
Chenxi Wang, Xiang Chen, Ningyu Zhang +4
Multimodal Large Language Models (MLLMs) frequently exhibit hallucination phenomena, but the underlying reasons remain poorly understood. In this paper, we present an empirical ana…
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Ningyu Zhang, Zekun Xi, Yujie Luo +11
Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs…
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng +6
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in kn…
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Jiaqi Li, Miaozeng Du, Chuanyi Zhang +6
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs). Despite its potential, current benchmarks…
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Ningyu Zhang, Bozhong Tian, Siyuan Cheng +6
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current a…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…
Can We Edit Multimodal Large Language Models?
Siyuan Cheng, Bozhong Tian, Qingbin Liu +4
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a hi…
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Peng Wang, Ningyu Zhang, Bozhong Tian +11
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to ou…
Editing Large Language Models: Problems, Methods, and Opportunities
Yunzhi Yao, Peng Wang, Bozhong Tian +5
Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a s…
Revisiting k-NN for Fine-tuning Pre-trained Language Models
Lei Li, Jing Chen, Bozhong Tian +1
Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP). In contrast, k-N…
Editing Language Model-based Knowledge Graph Embeddings
Siyuan Cheng, Ningyu Zhang, Bozhong Tian +3
Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deploy…