6 papers
ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests
Jingyuan He, Jiongnan Liu, Vishan Vishesh Oberoi +7
Recommender systems are among the most impactful AI applications, interacting with billions of users every day, guiding them to relevant products, services, or information tailored…
What Generative Search Engines Like and How to Optimize Web Content Cooperatively
Yujiang Wu, Shanshan Zhong, Yubin Kim +1
By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide signifi…
Semi-structured LLM Reasoners Can Be Rigorously Audited
Jixuan Leng, Cassandra A. Cohen, Zhixian Zhang +2
Although Large Language Models (LLMs) have become capable reasoners, the problem of faithfulness persists: their reasoning can contain errors and omissions that are difficult to de…
Generate, Not Recommend: Personalized Multimodal Content Generation
Jiongnan Liu, Zhicheng Dou, Ning Hu +1
To address the challenge of information overload from massive web contents, recommender systems are widely applied to retrieve and present personalized results for users. However,…
Respond Beyond Language: A Benchmark for Video Generation in Response to Realistic User Intents
Shuting Wang, Yunqi Liu, Zixin Yang +3
Querying generative AI models, e.g., large language models (LLMs), has become a prevalent method for information acquisition. However, existing query-answer datasets primarily focu…
Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models
Shuting Wang, Haihong Tang, Zhicheng Dou +1
The emergence of diffusion models (DMs) has significantly improved the quality of text-to-video generation models (VGMs). However, current VGM optimization primarily emphasizes the…