13 citations · 36 across the 12 of their papers we have counts for
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cs.CL2023
Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
Yerin Hwang, Yongil Kim, Hyunkyung Bae +3
To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been propose…
cs.CL2023
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…
cs.CL2023★ 2 cited
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
Dongryeol Lee, Segwang Kim, Minwoo Lee +4
Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have…