8 citations · 24 across the 5 of their papers we have counts for
9 papers · 1 filter
OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning
Seunghee Kim, Ingyu Bang, Seokgyu Jang +5
Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models…
Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
Cheonbok Park, Jeonghoon Kim, Joosung Lee +3
Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (…
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…
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…