7 citations · 12 across the 11 of their papers we have counts for
11 papers
Controllable Long-term Motion Generation with Extended Joint Targets
Eunjong Lee, Eunhee Kim, Sanghoon Hong +2
Generating stable and controllable character motion in real-time is a key challenge in computer animation. Existing methods often fail to provide fine-grained control or suffer fro…
EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
Kyunghoon Bae, Eunbi Choi, Kibong Choi +37
This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reaso…
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents
Jaeyoung Choe, Jihoon Kim, Woohwan Jung
Retrieval-augmented generation (RAG) based large language models (LLMs) are widely used in finance for their excellent performance on knowledge-intensive tasks. However, standardiz…
LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models
Yungi Kim, Hyunsoo Ha, Seonghoon Yang +3
Creating high-quality, large-scale datasets for large language models (LLMs) often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process ti…
Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs
Hyeonwoo Kim, Dahyun Kim, Jihoo Kim +3
The Open Ko-LLM Leaderboard has been instrumental in benchmarking Korean Large Language Models (LLMs), yet it has certain limitations. Notably, the disconnect between quantitative…
1 Trillion Token (1TT) Platform: A Novel Framework for Efficient Data Sharing and Compensation in Large Language Models
Chanjun Park, Hyunsoo Ha, Jihoo Kim +4
In this paper, we propose the 1 Trillion Token Platform (1TT Platform), a novel framework designed to facilitate efficient data sharing with a transparent and equitable profit-shar…
Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
Yungi Kim, Hyunsoo Ha, Sukyung Lee +3
With the increasing demand for substantial amounts of high-quality data to train large language models (LLMs), efficiently filtering large web corpora has become a critical challen…
SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models
Hyeonwoo Kim, Gyoungjin Gim, Yungi Kim +4
This study presents a novel learning approach designed to enhance both mathematical reasoning and problem-solving abilities of Large Language Models (LLMs). We focus on integrating…
Evalverse: Unified and Accessible Library for Large Language Model Evaluation
Jihoo Kim, Wonho Song, Dahyun Kim +3
This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly…
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Dahyun Kim, Chanjun Park, Sanghoon Kim +15
We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired…
X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity
Taejun Yun, Jinhyeon Kim, Deokyeong Kang +3
Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance on a task to a significant extent when evaluated in languages t…