I’m a Ph.D. student in Computer Science at Virginia Tech, advised by Tu Vu. I received my Master’s in Language Science and Technology from Saarland University, where I worked on efficient transfer learning and low-resource NLP with Dietrich Klakow and Vera Demberg. Earlier, I contributed to NLP research on historical archives at Academia Sinica, and I was selected as a Google CSRMP Fellow in 2023.

My research focuses on developing efficient, modular LLMs for multilingual, multitask, and multimodal deployments. I approach this through the lens of collaborative and communal machine learning. Specifically, I work on:

• Efficient model development: Developing methods to make alignment updates faster, cheaper, and more reusable, enabling continual adaptation across evolving model architectures.
• Parameter-efficient transfer learning: Modularizing task and document knowledge into reusable components for scalable transfer.
• Advanced reasoning: Building reasoning-capable multi-LLM systems and modular agents for complex, multilingual, and multimodal tasks.
• Instruction tuning: Enhancing LLM instruction-following capabilities to improve reasoning accuracy and factual responses.
• Data-centric methods: Designing data selection strategies to improve performance and efficiency in low-resource scenarios.

Seeking a 2025 internship position. My CV is available here.

🔥 News

• 2024.10: One paper accepted to EMNLP 2024 Industry Track.
• 2024.09: Paper Target-Aware Language Modeling via Granular Data Sampling accepted to EMNLP 2024.
• 2024.08: Started my Ph.D. at Virginia Tech.
• 2024.07: Paper Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning accepted to ACL 2024 SRW.
• 2024.03: Visiting Taipei during March–April — feel free to reach out if you’re there!
• 2024.02: Successfully defended my Master’s thesis Exploring Task Selection for Intermediate-Task Transfer Learning.
• 2024.02: Paper Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin accepted to LREC-COLING 2024.
• 2024.01: Paper Projecting Annotations for Discourse Relations accepted to CODI @ EACL 2024.

📝 Selected Publications

Please see Google Scholar for an up-to-date publication list.

* indicates equal contributions

Target-Aware Language Modeling via Granular Data Sampling
Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
EMNLP 2024
[Paper]
Using ~1% of RefinedWeb data to match full pretraining performance.

Exploring the Effectiveness and Consistency of Task Selection in Intermediate-Task Transfer Learning
Pin-Jie Lin, Miaoran Zhang, Marius Mosbach, Dietrich Klakow
Student Research Workshop at ACL 2024
[Paper] [Code]
Achieving robust results in modular selection via point-wise similarity.

Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin
Pin-Jie Lin, Merel Scholman, Muhammed Saeed, Vera Demberg
LREC-COLING 2024
[Paper]
Generating synthetic data from a phonological-theoretic, parameter-free framework.

In-Context Prompt Editing For Conditional Audio Generation
Ernie Chang*, Pin-Jie Lin*, Yang Li, Sidd Srinivasan, Gael Le Lan, David Kant, Yangyang Shi, Forrest Iandola, Vikas Chandra
ICASSP 2024
[Paper]
Featured in HuggingFace Daily Paper and selected by Jordi Pons.

Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
Pin-Jie Lin*, Muhammed Saeed*, Ernie Chang*, Merel Scholman
Interspeech 2023
[Paper]
Improving low-resource performance through mixed-language adaptation.

Revisiting Sample Size Determination in Natural Language Understanding
Ernie Chang*, Muhammad Hassan Rashid*, Pin-Jie Lin*, Changsheng Zhao, Vera Demberg, Yangyang Shi, Vikas Chandra
ACL 2023 Findings
[Paper] [Code]
Revisiting sample size estimation to improve data efficiency in NLU tasks.

Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization
Dongqi Pu*, Xudong Hong*, Pin-Jie Lin*, Ernie Chang, Vera Demberg
COLING 2022
[Paper]
Achieving top performance in movie script summarization.