25 citations · 99 across the 18 of their papers we have counts for
18 papers
AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence
Minbeom Kim, Hwanhee Lee, Joonsuk Park +2
As the integration of large language models into daily life is on the rise, there is a clear gap in benchmarks for advising on subjective and personal dilemmas. To address this, we…
LifeTox: Unveiling Implicit Toxicity in Life Advice
Minbeom Kim, Jahyun Koo, Hwanhee Lee +3
As large language models become increasingly integrated into daily life, detecting implicit toxicity across diverse contexts is crucial. To this end, we introduce LifeTox, a datase…
ProPILE: Probing Privacy Leakage in Large Language Models
Siwon Kim, Sangdoo Yun, Hwaran Lee +3
The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PI…
KoBBQ: Korean Bias Benchmark for Question Answering
Jiho Jin, Jiseon Kim, Nayeon Lee +3
The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts ot…
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…
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
Deokjae Lee, JunYeong Lee, Jung-Woo Ha +4
The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red…
Who Wrote this Code? Watermarking for Code Generation
Taehyun Lee, Seokhee Hong, Jaewoo Ahn +5
Since the remarkable generation performance of large language models raised ethical and legal concerns, approaches to detect machine-generated text by embedding watermarks are bein…
Critic-Guided Decoding for Controlled Text Generation
Minbeom Kim, Hwanhee Lee, Kang Min Yoo +3
Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinfor…
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
Hwanhee Lee, Kang Min Yoo, Joonsuk Park +2
Despite the recent advances in abstractive summarization systems, it is still difficult to determine whether a generated summary is factual consistent with the source text. To this…
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues
Miyoung Ko, Ingyu Seong, Hwaran Lee +3
With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do…
Plug-and-Play Adaptation for Continuously-updated QA
Kyungjae Lee, Wookje Han, Seung-won Hwang +3
Language models (LMs) have shown great potential as implicit knowledge bases (KBs). And for their practical use, knowledge in LMs need to be updated periodically. However, existing…
SUMBT+LaRL: Effective Multi-domain End-to-end Neural Task-oriented Dialog System
Hwaran Lee, Seokhwan Jo, HyungJun Kim +2
The recent advent of neural approaches for developing each dialog component in task-oriented dialog systems has remarkably improved, yet optimizing the overall system performance r…
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
Gi-Cheon Kang, Junseok Park, Hwaran Lee +2
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundament…
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
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. Previous neural approaches have modeled domain- and slot…
Unpaired Speech Enhancement by Acoustic and Adversarial Supervision for Speech Recognition
Geonmin Kim, Hwaran Lee, Bo-Kyeong Kim +2
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator. We point out several limitations of enhance…
Deep CNNs along the Time Axis with Intermap Pooling for Robustness to Spectral Variations
Hwaran Lee, Geonmin Kim, Ho-Gyeong Kim +2
Convolutional neural networks (CNNs) with convolutional and pooling operations along the frequency axis have been proposed to attain invariance to frequency shifts of features. How…
Compositional Sentence Representation from Character within Large Context Text
Geonmin Kim, Hwaran Lee, Jisu Choi +1
This paper describes a Hierarchical Composition Recurrent Network (HCRN) consisting of a 3-level hierarchy of compositional models: character, word and sentence. This model is desi…