Jiyeon Kim

Hello! I am a first-year Ph.D student at KAIST Graduate Schoold of AI. I am fortunate to be advised by Minjoon Seo in Language & Knowledge Lab. I am interested in life-long learning of AI models while interacting with dynamic environments. I have focused on understanding how Large Language Models acquire knowledge and why they lose learning capabilities as training progresses. By studying the learning dynamics of AI models, I aim to develop novel training methods that preserve capabilities, enhance complex reasoning, and integrate diverse inputs across multiple modalities. Previously, I obtained a Bachelor of Business Administration and Bachelor of Economics at Korea University. I worked as a junior economist at Bank of Korea for 7 years.

selected papers

Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition

Jiyeon Kim*, Hyunji Lee*, Hyowon Cho, Joel Jang, Hyeonbin Hwang, Seungpil Won, Youbin Ahn, Dohaeng Lee, Minjoon Seo

ICLR 2025 Oral

Abs Paper Code

In this work, we investigate how a model’s tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We introduce the concept of knowledge entropy, which quantifies the range of memory sources the model engages with; high knowledge entropy indicates that the model utilizes a wide range of memory sources, while low knowledge entropy suggests reliance on specific sources with greater certainty. Our analysis reveals a consistent decline in knowledge entropy as pretraining advances. We also find that the decline is closely associated with a reduction in the model’s ability to acquire and retain knowledge, leading us to conclude that diminishing knowledge entropy (smaller number of active memory sources) impairs the model’s knowledge acquisition and retention capabilities. We find further support for this by demonstrating that increasing the activity of inactive memory sources enhances the model’s capacity for knowledge acquisition and retention.
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?

Seongyun Lee*, Geewook Kim*, Jiyeon Kim*, Hyunji Lee, Hoyeon Chang, Sue Hyun Park, Minjoon Seo

ICLR 2025

Abs Paper

Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromises the inherent safety capabilities embedded in the original LLMs. Despite potential harmfulness due to weakened safety measures, in-depth analysis on the effects of VL adaptation on safety remains under-explored. This study examines how VL adaptation influences safety and evaluates the impact of safety fine-tuning methods. Our analysis reveals that safety degradation occurs during VL adaptation, even when the training data is safe. While safety tuning techniques like supervised fine-tuning with safety datasets or reinforcement learning from human feedback mitigate some risks, they still lead to safety degradation and a reduction in helpfulness due to over-rejection issues. Further analysis of internal model weights suggests that VL adaptation may impact certain safety-related layers, potentially lowering overall safety levels. Additionally, our findings demonstrate that the objectives of VL adaptation and safety tuning are divergent, which often results in their simultaneous application being suboptimal. To address this, we suggest the weight merging approach as an optimal solution effectively reducing safety degradation while maintaining helpfulness. These insights help guide the development of more reliable and secure LVLMs for real-world applications.
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval

Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Hyeongu Yun, Yireun Kim, Seung-won Hwang

ACL 2024

Abs Paper

We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an efficient inference framework for listwise ranking based on m-ary tournament sort with output caching. We evaluate and compare our model on the BEIR benchmark for zero-shot retrieval task, demonstrating that ListT5 (1) outperforms the state-of-the-art RankT5 baseline with a notable +1.3 gain in the average NDCG@10 score, (2) has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models, and (3) overcomes the lost-in-the-middle problem of previous listwise rerankers. Our code, model checkpoints, and the evaluation framework will be fully open-sourced.