6 papers
Controlling Output Rankings in Generative Engines for LLM-based Search
Haibo Jin, Ruoxi Chen, Peiyan Zhang +4
The way customers search for and choose products is changing with the rise of large language models (LLMs). LLM-based search, or generative engines, provides direct product recomme…
Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
Jiwei Guan, Haibo Jin, Haohan Wang
Recent advancements in Large Vision-Language Models (LVLMs) have shown groundbreaking capabilities across diverse multimodal tasks. However, these models remain vulnerable to adver…
GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing
Peiyan Zhang, Haibo Jin, Liying Kang +1
Jailbreak attacks reveal critical vulnerabilities in Large Language Models (LLMs) by causing them to generate harmful or unethical content. Evaluating these threats is particularly…
From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models
Haibo Jin, Peiyan Zhang, Peiran Wang +2
Large foundation models (LFMs) are susceptible to two distinct vulnerabilities: hallucinations and jailbreak attacks. While typically studied in isolation, we observe that defenses…
Reasoning Can Hurt the Inductive Abilities of Large Language Models
Haibo Jin, Peiyan Zhang, Man Luo +1
Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning - inferring latent rules from sparse examples - remains…
REVOLVE: Optimizing AI Systems by Tracking Response Evolution in Textual Optimization
Peiyan Zhang, Haibo Jin, Leyang Hu +5
Recent advancements in large language models (LLMs) have significantly enhanced the ability of LLM-based systems to perform complex tasks through natural language processing and to…