1 citations · 2 across the 12 of their papers we have counts for
14 papers
LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
Luyi Ma, Wanjia Sherry Zhang, Zezhong Fan +10
On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LL…
Robust Explanations for User Trust in Enterprise NLP Systems
Guilin Zhang, Kai Zhao, Jeffrey Friedman +3
Robust explanations are increasingly required for user trust in enterprise NLP, yet pre-deployment validation is difficult in the common case of black-box deployment (API-only acce…
Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
Yanan Cao, Farnaz Fallahi, Murali Mohana Krishna Dandu +9
Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains. Yet, their ability to infer temporal regularities from…
CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards
Zhiming Lin, Kai Zhao, Sophie Zhang +2
Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on…
No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
Tao Zhang, Kehui Yao, Luyi Ma +7
Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchma…
Beyond More Context: Retrieval Diversity Boosts Multi-Turn Intent Understanding
Zhiming Lin
Multi turn intent understanding is central to task oriented chatbots, yet real deployments face tight token budgets and noisy contexts, and most retrieval pipelines emphasize relev…
MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems
Shreeranjani Srirangamsridharan, Ali Abavisani, Reza Yousefi Maragheh +4
Meta titles and descriptions strongly shape engagement in search and recommendation platforms, yet optimizing them remains challenging. Search engine ranking models are black box e…
Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding
Vahid Mirjalili, Ramin Giahi, Sriram Kollipara +9
Spatial understanding is a critical capability for vision foundation models. While recent advances in large vision models or vision-language models (VLMs) have expanded recognition…
LayoutAgent: A Vision-Language Agent Guided Compositional Diffusion for Spatial Layout Planning
Zezhong Fan, Xiaohan Li, Luyi Ma +6
Designing realistic multi-object scenes requires not only generating images, but also planning spatial layouts that respect semantic relations and physical plausibility. On one han…
GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization
Luyi Ma, Wanjia Zhang, Kai Zhao +15
Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate…
CARTS: Collaborative Agents for Recommendation Textual Summarization
Jiao Chen, Kehui Yao, Reza Yousefi Maragheh +6
Current recommendation systems often require some form of textual data summarization, such as generating concise and coherent titles for product carousels or other grouped item dis…
CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
Najmeh Forouzandehmehr, Reza Yousefi Maragheh, Sriram Kollipara +4
Automated content-aware layout generation -- the task of arranging visual elements such as text, logos, and underlays on a background canvas -- remains a fundamental yet under-expl…
ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta +7
Retrieval-Augmented Generation (RAG) has shown promise in enhancing recommendation systems by incorporating external context into large language model prompts. However, existing RA…
LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification
Chun Liu, Hongguang Zhang, Kainan Zhao +2
With the booming of Large Language Models (LLMs), prompt-learning has become a promising method mainly researched in various research areas. Recently, many attempts based on prompt…