7 citations · 7 across the 8 of their papers we have counts for
8 papers
Argument Reconstruction as Supervision for Critical Thinking in LLMs
Hyun Ryu, Gyouk Chu, Gregor Betz +3
To think critically about arguments, human learners are trained to identify, reconstruct, and evaluate arguments. Argument reconstruction is especially important because it makes a…
SHRAG: AFrameworkfor Combining Human-Inspired Search with RAG
Hyunseok Ryu, Wonjune Shin, Hyun Park
Retrieval-Augmented Generation (RAG) is gaining recognition as one of the key technological axes for next generation information retrieval, owing to its ability to mitigate the hal…
ReviewScore: Misinformed Peer Review Detection with Large Language Models
Hyun Ryu, Doohyuk Jang, Hyemin S. Lee +16
Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-qua…
GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning
Woochang Sim, Hyunseok Ryu, Kyungmin Choi +2
The Abstraction and Reasoning Corpus (ARC) poses a stringent test of general AI capabilities, requiring solvers to infer abstract patterns from only a handful of examples. Despite…
Reasoning Model is Stubborn: Diagnosing Instruction Overriding in Reasoning Models
Doohyuk Jang, Yoonjeon Kim, Chanjae Park +2
Large language models have demonstrated remarkable proficiency in long and complex reasoning tasks. However, they frequently exhibit a problematic reliance on familiar reasoning pa…
Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding
Hyun Ryu, Eric Kim
Efficient inference in large language models (LLMs) has become a critical focus as their scale and complexity grow. Traditional autoregressive decoding, while effective, suffers fr…
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning
Hyun Ryu, Gyeongman Kim, Hyemin S. Lee +1
Complex logical reasoning tasks require a long sequence of reasoning, which a large language model (LLM) with chain-of-thought prompting still falls short. To alleviate this issue,…
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…