Hongzhi Yin
5 papers · Latest:
FINER-SQL: Boosting Small Language Models for Text-to-SQL
FINER-SQL boosts small language models for Text-to-SQL via fine-grained RL feedback, achieving LLM-level accuracy with lower latency.
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
ProMax uses LLM-derived profiles and distribution shaping to significantly improve recommender systems by guiding models to learn unseen item preferences.
Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
This paper introduces PUDA, a novel black-box attack framework that promotes items in LLM-based sequential recommenders without knowing the model or prompt.
ASPIRE: Make Spectral Graph Collaborative Filtering Great Again via Adaptive Filter Learning
ASPIRE introduces an adaptive filter learning framework for spectral graph collaborative filtering, overcoming manual tuning and "low-frequency explosion."
A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
This paper surveys and benchmarks deep learning for molecular property prediction, covering paradigms from quantum to foundation models.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.