5 citations · 17 across the 17 of their papers we have counts for
10 papers · 1 filter
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams
Jiyeon Kim, Hyunji Lee, Dylan Zhou +6
LLMs operating in dynamic real-world contexts often encounter knowledge that evolves continuously or emerges incrementally. To remain accurate and effective, models must adapt to n…
Understanding and Enhancing Mamba-Transformer Hybrids for Memory Recall and Language Modeling
Hyunji Lee, Wenhao Yu, Hongming Zhang +4
Hybrid models that combine state space models (SSMs) with attention mechanisms have shown strong performance by leveraging the efficiency of SSMs and the high recall ability of att…
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Seongyun Lee, Geewook Kim, Jiyeon Kim +4
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromise…
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
Jiyeon Kim, Hyunji Lee, Hyowon Cho +6
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,…
Semiparametric Token-Sequence Co-Supervision
Hyunji Lee, Doyoung Kim, Jihoon Jun +4
In this work, we introduce a semiparametric token-sequence co-supervision training method. It trains a language model by simultaneously leveraging supervision from the traditional…
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
Hanseok Oh, Hyunji Lee, Seonghyeon Ye +4
Despite the critical need to align search targets with users' intention, retrievers often only prioritize query information without delving into the users' intended search context.…
How Well Do Large Language Models Truly Ground?
Hyunji Lee, Sejune Joo, Chaeeun Kim +4
To reduce issues like hallucinations and lack of control in Large Language Models (LLMs), a common method is to generate responses by grounding on external contexts given as input,…
KTRL+F: Knowledge-Augmented In-Document Search
Hanseok Oh, Haebin Shin, Miyoung Ko +2
We introduce a new problem KTRL+F, a knowledge-augmented in-document search task that necessitates real-time identification of all semantic targets within a document with the aware…
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Sohee Yang, Jonghyeon Kim, Joel Jang +3
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal pro…
Cost-effective End-to-end Information Extraction for Semi-structured Document Images
Wonseok Hwang, Hyunji Lee, Jinyeong Yim +2
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its…