Papers by Zhuoran Lu
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)
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| Challenge: | Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. |
| Approach: | They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning. |
| Outcome: | The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights. |
Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations (2023.emnlp-main)
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| Challenge: | Recent studies have explored using large language models to generate synthetic datasets . however, the effectiveness of the LLM-generated synthetic data is inconsistent across different classification tasks. |
| Approach: | They propose to use large language models to generate synthetic datasets to better understand factors that moderate the effectiveness of LLM-generated synthetic data. |
| Outcome: | The results show that subjectivity is negatively associated with the performance of the model trained on synthetic data. |