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Kyubyung Chae

3 accepted papers

2025

Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs

EMNLP 2025

Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governments to develop their LLMs, tailored to their unique socio-cultural and historical contexts. However, there remains a sho

2024

Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information

NAACL 2024findings

A primary challenge in abstractive summarization is hallucination—the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that the domain (or topic) of the source text triggers the model to generate text that is highly probable in the domain, neglectin…

2024

Model-based Preference Optimization in Abstractive Summarization without Human Feedback

EMNLP 2024main

In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, although Large Language Models (LLMs) can generate fluent text, they often introduce inaccuracies by hallucinating conten…