Responsibilities include:

A comprehensive experiments for catasrophic forgetting in LLMs with task-specific fine-tuning.

• Deployed PEFT and LoRA for efficient fine-tuning.
• Using Bayesian methods to analyze the catasrophic forgetting.

Responsibilities include:

CLUE-ReDial: Leveraging Large Language Models for Generating Comprehensive Dataset with UrLs and Explanation

• Designed and implemented a multi-agent framework leveraging RAG and LLMs, achieving a 4× efficiency boost in generating interpretable recommendation explanations. Expanded the ReDial dataset to 40k high-quality utterances for fine-tuning conversational LLMs, enhancing domain-specific accuracy in movie recommendations.
• Designed a multi-agent framework, leveraging an early-stop module in the self-refine process to generate comprehensive and interpretable recommendation explanations, boosting transparency in recommendations.
• Applied web crawling techniques to create a movie dataset containing 25k entries, using Python, PyTorch, LaTeX, and the Singapore National High-Performance Computing (HPC) platform.

Responsibilities include: Worked on the OpenAlex and ORCID databases to analyze the relationship between university rankings and the mobility of researchers.

• Extracted education and hiring networks from research paper publications.
• Ranked Chinese universities based on extracted database information.
• Implemented a Multi-Variate Regression (MVR) model for ranking.
• Applied Markov Chain Monte Carlo (MCMC) and burn-in techniques to ensure the stability of convergence.
• Visualized researcher mobility and constructed a null model to compare the hierarchical structures of research institutions.