2 citations · 5 across the 13 of their papers we have counts for
6 papers · 1 filter
The Last Mile of Coding Agents for Long-Document Tasks: An Empirical Study on Clinical Trial Protocol Digitization
Jiacheng Lin, Geoffrey Low, Nishit Agarwal +4
Clinical trial protocols serve as the source of truth for study design, but are mostly written and used as unstructured PDF documents. Digitizing protocols turns static content int…
TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization
Jiacheng Lin, Zifeng Wang, Zheng Chen +5
Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on clinical development planni…
Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning
Jiacheng Lin, Zhenbang Wu, Jimeng Sun
We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). Wh…
GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data
Jiacheng Lin, Kun Qian, Haoyu Han +9
Graph-structured information offers rich contextual information that can enhance language models by providing structured relationships and hierarchies, leading to more expressive e…
Panacea: A foundation model for clinical trial search, summarization, design, and recruitment
Jiacheng Lin, Hanwen Xu, Zifeng Wang +2
Clinical trials are fundamental in developing new drugs, medical devices, and treatments. However, they are often time-consuming and have low success rates. Although there have bee…
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Pengcheng Jiang, Jiacheng Lin, Zifeng Wang +2
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). How…