1 citations · 1 across the 4 of their papers we have counts for
5 papers
Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It
Jonghyun Song, Haewon Park, Jeonghoon Shim +2
As LLM agents decide on users' behalf which product to buy, which hotel to book, or which paper to cite, a preference for items from certain sources (the sites or services they com…
Knowledge Editing for Masked Diffusion Language Models
Haewon Park, Yohan Jo
Knowledge editing aims to update or correct factual knowledge in a language model. A widely used approach, locate-then-edit, first localizes a fact within the model and then edits…
SUIT: Knowledge Editing with Subspace-Aware Key-Value Mappings
Haewon Park, Sangwoo Kim, Yohan Jo
Knowledge editing aims to efficiently correct factual errors in language models. Widely used locate-then-edit methods update an MLP layer by adjusting its weights to change the map…
Context-Robust Knowledge Editing for Language Models
Haewon Park, Gyubin Choi, Minjun Kim +1
Knowledge editing (KE) methods offer an efficient way to modify knowledge in large language models. Current KE evaluations typically assess editing success by considering only the…
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation
Injoon Hwang, Haewon Park, Youngwan Lee +2
Low-rank adaption (LoRA) is a prominent method that adds a small number of learnable parameters to the frozen pre-trained weights for parameter-efficient fine-tuning. Prompted by t…