6 citations · 8 across the 27 of their papers we have counts for
11 papers · 1 filter
Attack the Messages, Not the Agents: A Multi-round Adaptive Stealthy Tampering Framework for LLM-MAS
Bingyu Yan, Ziyi Zhou, Xiaoming Zhang +5
Large language model-based multi-agent systems (LLM-MAS) effectively accomplish complex and dynamic tasks through inter-agent communication, but this reliance introduces substantia…
Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning
Rui Pu, Chaozhuo Li, Rui Ha +3
Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, whic…
LLMs are Introvert
Litian Zhang, Xiaoming Zhang, Bingyu Yan +5
The exponential growth of social media and generative AI has transformed information dissemination, fostering connectivity but also accelerating the spread of misinformation. Under…
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…
Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction
Zhihao Tang, Chaozhuo Li, Litian Zhang +1
Early prediction of Mild Cognitive Impairment (MCI) conversion is hampered by a trade-off between immediacy--making fast predictions from a single baseline sMRI--and accuracy--leve…
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…
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…
MirrorShield: Towards Universal Defense Against Jailbreaks via Entropy-Guided Mirror Crafting
Rui Pu, Chaozhuo Li, Rui Ha +3
Defending large language models (LLMs) against jailbreak attacks is crucial for ensuring their safe deployment. Existing defense strategies typically rely on predefined static crit…
Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems
Bingyu Yan, Zhibo Zhou, Litian Zhang +6
Large language model-based multi-agent systems have recently gained significant attention due to their potential for complex, collaborative, and intelligent problem-solving capabil…