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20132025
most citedCrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models

16 citations · 19 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CL2025

Latent Reasoning via Sentence Embedding Prediction

Hyeonbin Hwang, Byeongguk Jeon, Seungone Kim +7

Autoregressive language models (LMs) generate one token at a time, yet human reasoning operates over higher-level abstractions - sentences, propositions, and concepts. This contras…

cs.CL2024

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,…

cs.CL2021★ 16 cited

CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models

Minwoo Lee, Seungpil Won, Juae Kim +3

Fact verification datasets are typically constructed using crowdsourcing techniques due to the lack of text sources with veracity labels. However, the crowdsourcing process often p…

cs.CL2020★ 2 cited

Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation

Yanghoon Kim, Seungpil Won, Seunghyun Yoon +1

Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing r…

cs.CL2018★ 1 cited

Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder

Seunghyun Yoon, Kunwoo Park, Joongbo Shin +4

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This…