5 citations · 7 across the 9 of their papers we have counts for
9 papers
DrugR: Optimizing Molecular Drugs through LLM-based Explicit Reasoning
Haoran Liu, Zheni Zeng, Yukun Yan +2
Molecule generation and optimization is a fundamental task in chemical domain. The rapid development of intelligent tools, especially large language models (LLMs) with powerful kno…
Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
Longfei Yun, Yihan Wu, Haoran Liu +13
Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this do…
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
Yingjian Chen, Haoran Liu, Yinhong Liu +7
Large Language Models (LLMs) show strong reasoning ability in open-domain question answering, yet their reasoning processes are typically linear and often logically inconsistent. I…
LLMs for Customized Marketing Content Generation and Evaluation at Scale
Haoran Liu, Amir Tahmasbi, Ehtesham Sam Haque +1
Offsite marketing is essential in e-commerce, enabling businesses to reach customers through external platforms and drive traffic to retail websites. However, most current offsite…
Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models
Maria Teleki, Xiangjue Dong, Haoran Liu +1
Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (…
MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering
Feiyang Li, Yingjian Chen, Haoran Liu +10
Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced m…
GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
Yingjian Chen, Haoran Liu, Yinhong Liu +8
Large language models (LLMs) are widely used, but they often generate subtle factual errors, especially in long-form text. These errors are fatal in some specialized domains such a…
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
Haoran Liu, Youzhi Luo, Tianxiao Li +2
We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e.g., Quantitative Estimate…
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Rui Yang, Haoran Liu, Edison Marrese-Taylor +8
Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine. However, the application of LLMs in real clinical sett…