Papers by Bolun Sun
Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise (2025.emnlp-main)
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Hanyin Wang, Chufan Gao, Qiping Xu, Bolun Liu, Guleid Hussein, Hariprasad Reddy Korsapati, Mohamad El Labban, Kingsley Iheasirim, Mohamed Hassan, Gokhan Anil, Brian Bartlett, Jimeng Sun
| Challenge: | Currently, no automated, scalable method exists to evaluate the quality of LLM-generated clinical notes, leaving manual evaluation the gold standard. |
| Approach: | They propose a framework for training PRMs to deliver step-level reward signals for LLM-generated clinical notes. |
| Outcome: | The proposed framework outperforms reasoning and non-reasoning models on key evaluations and selects physician-preferred clinical notes with 56.2% accuracy. |
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
| Approach: | They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation. |
| Outcome: | The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark. |
CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China (2026.acl-long)
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| Challenge: | Adaptive policy communication is a theory of governance in large, decentralized organizations where leaders exercise influence rather than precise control by combining clear and ambiguous instructions to calibrate discipline and flexibility. |
| Approach: | They propose an expert-directed annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling. |
| Outcome: | The proposed method achieves a Fleiss’ kappa of 0.86 on directive labels, indicating high reliability. |
Towards Adapting Open-Source Large Language Models for Expert-Level Clinical Note Generation (2025.findings-acl)
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Hanyin Wang, Chufan Gao, Bolun Liu, Qiping Xu, Guleid Hussein, Mohamad El Labban, Kingsley Iheasirim, Hariprasad Reddy Korsapati, Chuck Outcalt, Jimeng Sun
| Challenge: | Proprietary Large Language Models (LLMs) have demonstrated promising capabilities in clinical text summarization tasks. |
| Approach: | They propose a domain- and task-specific adaptation process for an open-source LLaMA-2 model . LLama-2 can generate high-quality clinical notes from outpatient patient-doctor dialogues . |
| Outcome: | The proposed model can generate clinical notes comparable to those authored by physicians. |