3 code implementations • 1 Apr 2024 • Yekyung Kim, Yapei Chang, Marzena Karpinska, Aparna Garimella, Varun Manjunatha, Kyle Lo, Tanya Goyal, Mohit Iyyer
While LLM-based auto-raters have proven reliable for factuality and coherence in other settings, we implement several LLM raters of faithfulness and find that none correlates strongly with human annotations, especially with regard to detecting unfaithful claims.
no code implementations • 9 Feb 2024 • Hochul Hwang, Sunjae Kwon, Yekyung Kim, Donghyun Kim
Safely navigating street intersections is a complex challenge for blind and low-vision individuals, as it requires a nuanced understanding of the surrounding context - a task heavily reliant on visual cues.
1 code implementation • 30 May 2023 • Jaehyung Kim, Yekyung Kim, Karin de Langis, Jinwoo Shin, Dongyeop Kang
However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy.
no code implementations • 28 Dec 2021 • Yekyung Kim, Seohyeong Jeong, Kyunghyun Cho
Despite the success of mixup in data augmentation, its applicability to natural language processing (NLP) tasks has been limited due to the discrete and variable-length nature of natural languages.
no code implementations • AACL (lifelongnlp) 2020 • Yekyung Kim
Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency.