12 papers
Unlocking Multimodal Protein Language Models at Inference Time
Yi Zhou, Qipeng Wang, Yunqing Liu +3
Multimodal protein language models (pLMs) learn joint protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sam…
FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering
Yujuan Ding, Linyin Luo, Shijie Wang +5
Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transforme…
A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing
Zhuohang Jiang, Yuxin Chen, Yongsen Pan +6
Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation. Traditional tuning requires experts to repeatedly design strategies, configure exper…
Diffusion Language Model for Recommendation
Chengyi Liu, Yongqi Zhou, Junwei Pan +8
Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generati…
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
Zhuohang Jiang, Yuxin Chen, Shijie Wang +6
Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent o…
Inference Cost Attacks for Retrieval-Augmented Large Language Models
Chengliang Liu, Liangbo Ning, Yujuan Ding +1
Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamica…