11 papers
Rethinking Fairness in LLM-Based Recommender Systems: A Survey
Song-Duo Ma, Chu-Yun Chen, Bang-An Li +3
Large Language Models (LLMs) are reshaping recommender systems by enabling more semantic, generative, and interactive recommendation pipelines. However, this shift also introduces…
RADAR: Retrieval-Augmented Detector with Adversarial Refinement for Robust Fake News Detection
Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin +4
To efficiently combat the spread of LLM-generated misinformation, we present RADAR, a Retrieval-Augmented Detector with Adversarial Refinement for robust fake news detection. Our a…
Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection
Minseok Jung, Cynthia Fuertes Panizo, Liam Dugan +4
The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in r…
The Trojan Knowledge: Bypassing Commercial LLM Guardrails via Harmless Prompt Weaving and Adaptive Tree Search
Rongzhe Wei, Peizhi Niu, Xinjie Shen +7
Large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safety guardrails to elicit harmful outputs. Existing approaches overwhelmingly operate within the p…
Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models
Huzaifa Arif, Pin-Yu Chen, Alex Gittens +2
With the increasing reliance on AI models for weather forecasting, it is imperative to evaluate their vulnerability to adversarial perturbations. This work introduces Weather Adapt…
Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization
Xurui Li, Kaisong Song, Rui Zhu +2
Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks. Existing works tend to focus on either i…