no code implementations • 2 May 2016 • Geonmin Kim, Hwaran Lee, Jisu Choi, Soo-Young Lee
In the HCRN, word representations are built from characters, thus resolving the data-sparsity problem, and inter-sentence dependency is embedded into sentence representation at the level of sentence composition.
no code implementations • 10 Jun 2016 • Hwaran Lee, Geonmin Kim, Ho-Gyeong Kim, Sang-Hoon Oh, Soo-Young Lee
Convolutional neural networks (CNNs) with convolutional and pooling operations along the frequency axis have been proposed to attain invariance to frequency shifts of features.
1 code implementation • 6 Nov 2018 • Geonmin Kim, Hwaran Lee, Bo-Kyeong Kim, Sang-Hoon Oh, Soo-Young Lee
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator.
3 code implementations • ACL 2019 • Hwaran Lee, Jinsik Lee, Tae-Yoon Kim
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn.
Ranked #17 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.0
1 code implementation • Findings (EMNLP) 2021 • Gi-Cheon Kang, Junseok Park, Hwaran Lee, Byoung-Tak Zhang, Jin-Hwa Kim
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
no code implementations • 22 Sep 2020 • Hwaran Lee, Seokhwan Jo, HyungJun Kim, SangKeun Jung, Tae-Yoon Kim
To our best knowledge, our work is the first comprehensive study of a modularized E2E multi-domain dialog system that learning from each component to the entire dialog policy for task success.
no code implementations • Findings (ACL) 2022 • Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee
To this end, we first propose a novel task--Continuously-updated QA (CuQA)--in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.
1 code implementation • Findings (NAACL) 2022 • Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung
To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries.
1 code implementation • 24 May 2022 • Miyoung Ko, Ingyu Seong, Hwaran Lee, Joonsuk Park, Minsuk Chang, Minjoon Seo
With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence.
no code implementations • 21 Dec 2022 • Minbeom Kim, Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung
In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.
1 code implementation • 24 May 2023 • Taehyun Lee, Seokhee Hong, Jaewoo Ahn, Ilgee Hong, Hwaran Lee, Sangdoo Yun, Jamin Shin, Gunhee Kim
Based on \citet{Kirchenbauer2023watermark}, we propose a new watermarking method, Selective WatErmarking via Entropy Thresholding (SWEET), that promotes "green" tokens only at the position with high entropy of the token distribution during generation, thereby preserving the correctness of the generated code.
1 code implementation • 27 May 2023 • Deokjae Lee, JunYeong Lee, Jung-Woo Ha, Jin-Hwa Kim, Sang-Woo Lee, Hwaran Lee, Hyun Oh Song
To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations.
1 code implementation • 28 May 2023 • Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Gunhee Kim, Jung-Woo Ha
Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.
1 code implementation • 28 May 2023 • Hwaran Lee, Seokhee Hong, Joonsuk Park, Takyoung Kim, Meeyoung Cha, Yejin Choi, Byoung Pil Kim, Gunhee Kim, Eun-Ju Lee, Yong Lim, Alice Oh, Sangchul Park, Jung-Woo Ha
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising.
no code implementations • 31 Jul 2023 • Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo, Alice Oh, Hwaran Lee
In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset.
1 code implementation • 12 Oct 2023 • Seungone Kim, Jamin Shin, Yejin Cho, Joel Jang, Shayne Longpre, Hwaran Lee, Sangdoo Yun, Seongjin Shin, Sungdong Kim, James Thorne, Minjoon Seo
We first construct the Feedback Collection, a new dataset that consists of 1K fine-grained score rubrics, 20K instructions, and 100K responses and language feedback generated by GPT-4.
no code implementations • 16 Nov 2023 • Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial.
1 code implementation • 20 Feb 2024 • Martin Gubri, Dennis Ulmer, Hwaran Lee, Sangdoo Yun, Seong Joon Oh
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them.
no code implementations • 21 Feb 2024 • Jiyoung Lee, Minwoo Kim, Seungho Kim, Junghwan Kim, Seunghyun Won, Hwaran Lee, Edward Choi
For the common knowledge dataset, we constructed samples based on Korean textbooks and GED reference materials.
1 code implementation • 9 Mar 2024 • Dennis Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun, Seong Joon Oh
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important.
no code implementations • NAACL (GeBNLP) 2022 • Jaimeen Ahn, Hwaran Lee, JinHwa Kim, Alice Oh
Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model. However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model. This paper studies what causes gender bias to increase after the knowledge distillation process. Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation. By doing so, we can significantly reduce the gender bias amplification after knowledge distillation. We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.