32 citations · 43 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 8 cited
Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2
Hamish Ivison, Yizhong Wang, Valentina Pyatkin +8
Since the release of TÜLU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques. We test and inco…
cs.CL2022★ 32 cited
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Joel Jang, Seonghyeon Ye, Minjoon Seo
Previous work has shown that there exists a scaling law between the size of Language Models (LMs) and their zero-shot performance on different downstream NLP tasks. In this work, w…
cs.LG2020★ 3 cited
Sequential Targeting: an incremental learning approach for data imbalance in text classification
Joel Jang, Yoonjeon Kim, Kyoungho Choi +1
Classification tasks require a balanced distribution of data to ensure the learner to be trained to generalize over all classes. In real-world datasets, however, the number of inst…