277 citations · 1.6k across the 56 of their papers we have counts for
14 papers · 1 filter
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Hwaran Lee, Seokhee Hong, Joonsuk Park +3
Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critica…
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
Hwaran Lee, Seokhee Hong, Joonsuk Park +10
The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with thi…
Continuous Decomposition of Granularity for Neural Paraphrase Generation
Xiaodong Gu, Zhaowei Zhang, Sang-Woo Lee +2
While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior…
Two-Step Question Retrieval for Open-Domain QA
Yeon Seonwoo, Juhee Son, Jiho Jin +4
The retriever-reader pipeline has shown promising performance in open-domain QA but suffers from a very slow inference speed. Recently proposed question retrieval models tackle thi…
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model
Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn +8
Many recent studies on large-scale language models have reported successful in-context zero- and few-shot learning ability. However, the in-depth analysis of when in-context learni…
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
Boseop Kim, HyoungSeok Kim, Sang-Woo Lee +34
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less report…
Weakly Supervised Pre-Training for Multi-Hop Retriever
Yeon Seonwoo, Sang-Woo Lee, Ji-Hoon Kim +2
In multi-hop QA, answering complex questions entails iterative document retrieval for finding the missing entity of the question. The main steps of this process are sub-question de…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…
Reward Optimization for Neural Machine Translation with Learned Metrics
Raphael Shu, Kang Min Yoo, Jung-Woo Ha
Neural machine translation (NMT) models are conventionally trained with token-level negative log-likelihood (NLL), which does not guarantee that the generated translations will be…
DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
Xiaodong Gu, Kang Min Yoo, Jung-Woo Ha
Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear seque…
Context-Aware Answer Extraction in Question Answering
Yeon Seonwoo, Ji-Hoon Kim, Jung-Woo Ha +1
Extractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the corr…
ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding
Minjeong Kim, Gyuwan Kim, Sang-Woo Lee +1
Language model pre-training has shown promising results in various downstream tasks. In this context, we introduce a cross-modal pre-trained language model, called Speech-Text BERT…
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Sang-Woo Lee, Tong Gao, Sohee Yang +2
Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that…
DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder
Xiaodong Gu, Kyunghyun Cho, Jung-Woo Ha +1
Variational autoencoders~(VAEs) have shown a promise in data-driven conversation modeling. However, most VAE conversation models match the approximate posterior distribution over t…