6 papers
Detecting AI-Generated Content on Social Media with Multi-modal Language Models
Chenyang Yang, Shen Yan, Yibo Yang +13
Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, an…
Verifiable Reasoning for LLM-based Generative Recommendation
Xinyu Lin, Hanqing Zeng, Hanchao Yu +8
Reasoning in Large Language Models (LLMs) has recently shown strong potential in enhancing generative recommendation through deep understanding of complex user preference. Existing…
Reason to Contrast: A Cascaded Multimodal Retrieval Framework
Xuanming Cui, Hong-You Chen, Hao Yu +10
Traditional multimodal retrieval systems rely primarily on bi-encoder architectures, where performance is closely tied to embedding dimensionality. Recent work, Think-Then-Embed (T…
Optimizing Recall or Relevance? A Multi-Task Multi-Head Approach for Item-to-Item Retrieval in Recommendation
Jiang Zhang, Sumit Kumar, Wei Chang +7
The task of item-to-item (I2I) retrieval is to identify a set of relevant and highly engaging items based on a given trigger item. It is a crucial component in modern recommendatio…
Inference Compute-Optimal Video Vision Language Models
Peiqi Wang, ShengYun Peng, Xuewen Zhang +5
This work investigates the optimal allocation of inference compute across three key scaling factors in video vision language models: language model size, frame count, and the numbe…
Towards An Efficient LLM Training Paradigm for CTR Prediction
Allen Lin, Renqin Cai, Yun He +5
Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significa…