89 citations · 144 across the 22 of their papers we have counts for
7 papers · 1 filter
Personalized Language Models via Privacy-Preserving Evolutionary Model Merging
Kyuyoung Kim, Jinwoo Shin, Jaehyung Kim
Personalization in language models aims to tailor model behavior to individual users or user groups. Prompt-based methods incorporate user preferences into queries, while training-…
Tabular Transfer Learning via Prompting LLMs
Jaehyun Nam, Woomin Song, Seong Hyeon Park +5
Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the…
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Jaehyung Kim, Jaehyun Nam, Sangwoo Mo +5
Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new infor…
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
Jaehyung Kim, Yekyung Kim, Karin de Langis +2
The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some m…
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
Jaehyung Kim, Jinwoo Shin, Dongyeop Kang
The development of largely human-annotated benchmarks has driven the success of deep neural networks in various NLP tasks. To enhance the effectiveness of existing benchmarks, coll…
Annotation Imputation to Individualize Predictions: Initial Studies on Distribution Dynamics and Model Predictions
London Lowmanstone, Ruyuan Wan, Risako Owan +2
Annotating data via crowdsourcing is time-consuming and expensive. Due to these costs, dataset creators often have each annotator label only a small subset of the data. This leads…
Everyone's Voice Matters: Quantifying Annotation Disagreement Using Demographic Information
Ruyuan Wan, Jaehyung Kim, Dongyeop Kang
In NLP annotation, it is common to have multiple annotators label the text and then obtain the ground truth labels based on the agreement of major annotators. However, annotators a…