3 citations · 3 across the 11 of their papers we have counts for
5 papers · 1 filter
What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
Joosung Lee, Hwiyeol Jo, Donghyeon Ko +3
While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment bet…
KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs
Donghyeon Ko, Yeguk Jin, Kyubyung Chae +6
We present , a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is d…
Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs
Kyubyung Chae, Gihoon Kim, Gyuseong Lee +3
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 governme…
Model-based Preference Optimization in Abstractive Summarization without Human Feedback
Jaepill Choi, Kyubyung Chae, Jiwoo Song +2
In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, a…
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
Kyubyung Chae, Jaepill Choi, Yohan Jo +1
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 th…