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Patrick Sutanto

2 accepted papers

2025

LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering

EMNLP 2025

Encoder models offer efficiency for specific tasks, but their performance depend on data availability. While Large Language Models (LLMs) excel at few-shot learning, their direct application in real-world scenarios is often hindered by their high computational cost. To address this challenge, we pro

Cited by 0SourcePDFScholar
2024

Pushing the Limits of Low-Resource NER Using LLM Artificial Data Generation

ACL 2024findings

Named Entity Recognition (NER) is an important task, but to achieve great performance, it is usually necessary to collect a large amount of labeled data, incurring high costs. In this paper, we propose using open-source Large Language Models (LLM) to generate NER data with only a few labeled example…