2 citations · 2 across the 8 of their papers we have counts for
8 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…
K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology
Soyeon Kim, Cheongwoong Kang, Myeongjin Lee +3
The development of practical (multimodal) large language model assistants for Korean weather forecasters is hindered by the absence of a multidimensional, expert-level evaluation f…
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…
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning
Anh Tong, Thanh Nguyen-Tang, Dongeun Lee +5
Recent advancements in large language models (LLMs) based on transformer architectures have sparked significant interest in understanding their inner workings. In this paper, we in…
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…
Impact of Co-occurrence on Factual Knowledge of Large Language Models
Cheongwoong Kang, Jaesik Choi
Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co…