Hazel H. Kim — accepted AI-conference papers

2025

How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty

COLING 2025main

The quality of rationales is essential in the reasoning capabilities of language models. Rationales not only enhance reasoning performance in complex natural language tasks but also justify model decisions. However, obtaining impeccable rationales is often impossible. Our study aims to investigate h…

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2022

ALP: Data Augmentation Using Lexicalized PCFGs for Few-Shot Text Classification

Hazel H. Kim, Daecheol Woo, Seong Joon Oh, Jeong-Won Cha, Yo-Sub Han

AAAI 2022technical

Data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural lang…

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