Papers by Zheheng Luo
Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to optimise pretraining performance have not addressed the complexities of domain-adaptive continual pretraining. |
| Approach: | They propose a framework that dynamically assesses learning velocity and adjusts data proportions accordingly, favouring slower learning domains while de-emphasising faster learning ones. |
| Outcome: | The proposed framework achieves performance gains in math and code reasoning tasks and command-line generation benchmarks. |
EMPEC: A Comprehensive Benchmark for Evaluating Large Language Models Across Diverse Healthcare Professions (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) show their potential in accurately answering biomedical questions, yet current healthcare benchmarks primarily assess knowledge mastered by medical doctors, neglecting other essential professions. |
| Approach: | They evaluated 17 LLMs including proprietary and open-source models and found they struggled with specialized fields and alternative medicine. |
| Outcome: | The examinations for medical PErsonnel in Chinese (EMPEC) features 157,803 exam questions across 124 subjects and 20 healthcare professions. |
Readability Controllable Biomedical Document Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing controllable summarization systems for biomedical documents have little attention to readability control, leaving users with incompatible summaries . |
| Approach: | They propose a task of readability controllable summarization for biomedical documents to generate summaries that are incompatible with users' levels of expertise. |
| Outcome: | The proposed model is based on pre-trained language models with prevalent controlling and generation techniques and evaluates the readability discrepancy between lay and technical summaries. |
ELAINE-medLLM: Lightweight English Japanese Chinese Trilingual Large Language Model for Bio-medical Domain (2025.coling-main)
Copied to clipboard
Ken Yano, Zheheng Luo, Jimin Huang, Qianqian Xie, Masaki Asada, Chenhan Yuan, Kailai Yang, Makoto Miwa, Sophia Ananiadou, Jun’ichi Tsujii
| Challenge: | Existing bilingual or multilingual medical LLMs are limited in multilingual data and therefore perform poorly in non-English languages such as Japanese and Chinese. |
| Approach: | They propose to use a trilingual (English, Japanese, Chinese) large language model adapted for the bio-medical domain to harness the knowledge and abilities of the base model. |
| Outcome: | The proposed model can support English, Japanese, and Chinese and is adapted for a bio-medical domain. |
Process-based Self-Rewarding Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to reward LLMs' outputs are not effective in mathematical reasoning scenarios and may lead to a decline in performance. |
| Approach: | They propose a process-based self-rewarding pipeline that integrates long-thought reasoning, step-wise LLM-as-a-Judge, and step- wise preference optimization within the existing paradigm. |
| Outcome: | The proposed model improves the performance of Large Language Models on multiple mathematical reasoning benchmarks and shows that it can surpass human capabilities. |