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Naun Kang

2 accepted papers

2026

Understanding LoRA as Knowledge Memory: An Empirical Analysis

ICML 2026poster

Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging. Although inference-time methods like In-Context Learning (ICL) and Retrieval-Augmented Generation (RAG) are popular, they face constraints in context budgets, costs, and retr…

Cited by 0SourceScholar
2025

PLEX: Adaptive Parameter-Efficient Fine-Tuning for Code LLMs using Lottery-Tickets

NAACL 2025industry

Fine-tuning large language models (LLMs) for code generation is challenging due to computational costs and the underrepresentation of some programming languages (PLs) in pre-training. We propose PLEX, a lottery-ticket based parameter-efficient fine-tuning (PEFT) method that adapts LLMs to either wel…