1 citations · 1 across the 7 of their papers we have counts for
7 papers
Lifted State Hypothesis in Large Language Models
Bumjin Park, Jaesik Choi
Large language models (LLMs) adapt rapidly through fine-tuning and in-context learning, yet it remains unclear which inputs they treat as the same case and why their predictions ch…
Incomplete Prompt Jailbreaks in Large Language Models
Yeonjea Kim, Bumjin Park, Jaesik Choi
Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. Nevertheless, sentence completion remains vulnerable to incom…
Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models
Bumjin Park, Jinsil Lee, Jaesik Choi
Large language models (LLMs) are increasingly engaging in moral and ethical reasoning, where criteria for judgment are often unclear, even for humans. While LLM alignment studies c…
Identifying the Source of Generation for Large Language Models
Bumjin Park, Jaesik Choi
Large language models (LLMs) memorize text from several sources of documents. In pretraining, LLM trains to maximize the likelihood of text but neither receives the source of the t…
Memorizing Documents with Guidance in Large Language Models
Bumjin Park, Jaesik Choi
Training data plays a pivotal role in AI models. Large language models (LLMs) are trained with massive amounts of documents, and their parameters hold document-related contents. Re…
Explanation on Pretraining Bias of Finetuned Vision Transformer
Bumjin Park, Jaesik Choi
As the number of fine tuning of pretrained models increased, understanding the bias of pretrained model is essential. However, there is little tool to analyse transformer architect…
Edge-oriented Implicit Neural Representation with Channel Tuning
Wonjoon Chang, Dahee Kwon, Bumjin Park
Implicit neural representation, which expresses an image as a continuous function rather than a discrete grid form, is widely used for image processing. Despite its outperforming r…