121 citations · 316 across the 60 of their papers we have counts for
16 papers · 1 filter
Just-In-Time Agent Memory with Runtime Agentic Research
Bingyu Yan, Chaofan Li, Hongjin Qian +3
Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific request arrives. While this reduces…
CIBuzzBench: A Benchmark for Cross-Lingual Understanding of Chinese Internet Buzzwords
Yifan Wang, Junyu Lu, Qifan Wang +6
Chinese social media has generated a vast and continually evolving lexicon of internet buzzwords whose meanings are often non-literal and deeply rooted in local cultural and pragma…
Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability
Zejian Chen, Zhanyuan Liu, Chaozhuo Li +6
Computer-use agents(CUAs)are moving frombounded benchmarks toward real software environments, wherethey operate browsers, desktops, mobile applications, flesystems,terminals, and t…
LANCET: Neural Intervention via Structural Entropy for Mitigating Faithfulness Hallucinations in LLMs
Chenxu Wang, Chaozhuo Li, Pengbo Wang +7
Large Language Models have revolutionized information processing, yet their reliability is severely compromised by faithfulness hallucinations. While current approaches attempt to…
Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models
Chaozhuo Li, Pengbo Wang, Chenxu Wang +7
Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions…
Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection
Ziyi Zhou, Xiaoming Zhang, Litian Zhang +4
The widespread dissemination of fake news on social media has significantly impacted society, resulting in serious consequences. Conventional deep learning methodologies employing…
The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
Songyang Liu, Chaozhuo Li, Jiameng Qiu +5
With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), including content gener…
One SPACE to Rule Them All: Jointly Mitigating Factuality and Faithfulness Hallucinations in LLMs
Pengbo Wang, Chaozhuo Li, Chenxu Wang +3
LLMs have demonstrated unprecedented capabilities in natural language processing, yet their practical deployment remains hindered by persistent factuality and faithfulness hallucin…
Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge
Yi Sui, Chaozhuo Li, Chen Zhang +2
Retrieval-augmented generation (RAG) aims to mitigate the hallucination of Large Language Models (LLMs) by retrieving and incorporating relevant external knowledge into the generat…
Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection
Ziyi Zhou, Xiaoming Zhang, Shenghan Tan +2
The proliferation of fake news on social media platforms has exerted a substantial influence on society, leading to discernible impacts and deleterious consequences. Conventional d…
Beyond Entity Alignment: Towards Complete Knowledge Graph Alignment via Entity-Relation Synergy
Xiaohan Fang, Chaozhuo Li, Yi Zhao +5
Knowledge Graph Alignment (KGA) aims to integrate knowledge from multiple sources to address the limitations of individual Knowledge Graphs (KGs) in terms of coverage and depth. Ho…
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection
Ziyi Zhou, Xiaoming Zhang, Litian Zhang +6
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content. However, these benchmarks typ…
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Rui Li, Xu Chen, Chaozhuo Li +8
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize int…
Generative Sentiment Transfer via Adaptive Masking
Yingze Xie, Jie Xu, LiQiang Qiao +3
Sentiment transfer aims at revising the input text to satisfy a given sentiment polarity while retaining the original semantic content. The nucleus of sentiment transfer lies in pr…
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
Junhan Yang, Zheng Liu, Shitao Xiao +6
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Rec…
Detecting Context Dependent Messages in a Conversational Environment
Chaozhuo Li, Yu Wu, Wei Wu +3
While automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic co…