22 citations · 46 across the 11 of their papers we have counts for
12 papers
KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs
Donghyeon Ko, Yeguk Jin, Kyubyung Chae +6
We present , a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is d…
Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts
Sang-Woo Lee, Sohee Yang, Donghyun Kwak +1
Many recent papers have studied the development of superforecaster-level event forecasting LLMs. While methodological problems with early studies cast doubt on the use of LLMs for…
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
Sanghwan Bae, Jiwoo Hong, Min Young Lee +3
Recent advances in reinforcement learning with verifiable rewards (RLVR) show that large language models enhance their reasoning abilities when trained with verifiable signals. How…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
Aligning Large Language Models through Synthetic Feedback
Sungdong Kim, Sanghwan Bae, Jamin Shin +4
Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs. However, it requires significant human demonst…
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?
Sang-Woo Lee, Sungdong Kim, Donghyeon Ko +9
Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., s…
Keep Me Updated! Memory Management in Long-term Conversations
Sanghwan Bae, Donghyun Kwak, Soyoung Kang +7
Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal wi…
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
Sanghwan Bae, Donghyun Kwak, Sungdong Kim +4
Recent open-domain dialogue models have brought numerous breakthroughs. However, building a chat system is not scalable since it often requires a considerable volume of human-human…
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…
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding
Seunghyun Seo, Donghyun Kwak, Bowon Lee
Most End-to-End (E2E) SLU networks leverage the pre-trained ASR networks but still lack the capability to understand the semantics of utterances, crucial for the SLU task. To solve…
Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation
Won Ik Cho, Donghyun Kwak, Ji Won Yoon +1
Speech is one of the most effective means of communication and is full of information that helps the transmission of utterer's thoughts. However, mainly due to the cumbersome proce…
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation
Kyung-Min Kim, Donghyun Kwak, Hanock Kwak +7
Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social gra…