9 papers
Generative Conversational Recommender System
Sixiao Zhang, Mingrui Liu, Cheng Long
Conversational recommender systems aim to provide personalized recommendations via natural language interactions. However, existing approaches either decouple recommendation from d…
The Trojan Example: Jailbreaking LLMs through Template Filling and Unsafety Reasoning
Mingrui Liu, Sixiao Zhang, Cheng Long +1
As Large Language Models (LLMs) become integral to computing infrastructure, safety alignment serves as the primary security control preventing the generation of harmful payloads.…
RedVisor: Reasoning-Aware Prompt Injection Defense via Zero-Copy KV Cache Reuse
Mingrui Liu, Sixiao Zhang, Cheng Long +1
Large Language Models (LLMs) are increasingly vulnerable to Prompt Injection (PI) attacks, where adversarial instructions hidden within retrieved contexts hijack the model's execut…
Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation
Mingrui Liu, Sixiao Zhang, Cheng Long
Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding v…
Wukong Framework for Not Safe For Work Detection in Text-to-Image systems
Mingrui Liu, Sixiao Zhang, Cheng Long
Text-to-Image (T2I) generation is a popular AI-generated content (AIGC) technology enabling diverse and creative image synthesis. However, some outputs may contain Not Safe For Wor…
On Mitigating Data Sparsity in Conversational Recommender Systems
Sixiao Zhang, Mingrui Liu, Cheng Long +4
Conversational recommender systems (CRSs) infer user preferences from dialogue contexts, but they suffer from severe data sparsity in both dialogue and entity spaces. Dialogue data…