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Collections including paper arxiv:2404.01954

HyperCLOVA X SEED

Collection

HyperCLOVA X SEED is NAVER's lightweight open-source lineup with a strong focus on Korean language performance • 6 items • Updated Dec 24, 2025 • 42
HyperCLOVA X 8B Omni

Paper • 2601.01792 • Published Jan 5
HyperCLOVA X THINK Technical Report

Paper • 2506.22403 • Published Jun 27, 2025
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Multilingual - Benchmarks

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
ByT5: Towards a token-free future with pre-trained byte-to-byte models

Paper • 2105.13626 • Published May 28, 2021 • 5
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published Dec 13, 2024 • 108

Long-context LLMs Struggle with Long In-context Learning

Paper • 2404.02060 • Published Apr 2, 2024 • 37
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Text - Supervised Fine-tuning

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Pre-training - In-filling - PSM and SPM ordering

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

한국 LLM 논문

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

Paper • 2408.02545 • Published Aug 5, 2024 • 40
HyperCLOVA X THINK Technical Report

Paper • 2506.22403 • Published Jun 27, 2025
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
KMMLU: Measuring Massive Multitask Language Understanding in Korean

Paper • 2402.11548 • Published Feb 18, 2024

Papers - Fine-tuning - PPO

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
UltraFeedback: Boosting Language Models with High-quality Feedback

Paper • 2310.01377 • Published Oct 2, 2023 • 6
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

Paper • 2305.14387 • Published May 22, 2023 • 1
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5, 2024 • 145

Papers - Text - Supervised Fine-tuning - Batch Grouping

Batches are grouped by similar token length to help optimize gpu/hardware. Mini batch lengths are different but the max number of tokens is the same.

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Pre-training - Dynamic Context Length

For HyperClova X they split 90% at 4096 and 10% at 32k context length during pt

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28, 2024 • 14
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
Instruction Tuning with Human Curriculum

Paper • 2310.09518 • Published Oct 14, 2023 • 3

HyperCLOVA X SEED

Collection

HyperCLOVA X SEED is NAVER's lightweight open-source lineup with a strong focus on Korean language performance • 6 items • Updated Dec 24, 2025 • 42
HyperCLOVA X 8B Omni

Paper • 2601.01792 • Published Jan 5
HyperCLOVA X THINK Technical Report

Paper • 2506.22403 • Published Jun 27, 2025
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

한국 LLM 논문

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

Paper • 2408.02545 • Published Aug 5, 2024 • 40
HyperCLOVA X THINK Technical Report

Paper • 2506.22403 • Published Jun 27, 2025
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
KMMLU: Measuring Massive Multitask Language Understanding in Korean

Paper • 2402.11548 • Published Feb 18, 2024

Papers - Multilingual - Benchmarks

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
ByT5: Towards a token-free future with pre-trained byte-to-byte models

Paper • 2105.13626 • Published May 28, 2021 • 5
Byte Latent Transformer: Patches Scale Better Than Tokens

Paper • 2412.09871 • Published Dec 13, 2024 • 108

Papers - Fine-tuning - PPO

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
UltraFeedback: Boosting Language Models with High-quality Feedback

Paper • 2310.01377 • Published Oct 2, 2023 • 6
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

Paper • 2305.14387 • Published May 22, 2023 • 1
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5, 2024 • 145

Long-context LLMs Struggle with Long In-context Learning

Paper • 2404.02060 • Published Apr 2, 2024 • 37
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Text - Supervised Fine-tuning - Batch Grouping

Batches are grouped by similar token length to help optimize gpu/hardware. Mini batch lengths are different but the max number of tokens is the same.

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Text - Supervised Fine-tuning

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Pre-training - Dynamic Context Length

For HyperClova X they split 90% at 4096 and 10% at 32k context length during pt

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Papers - Pre-training - In-filling - PSM and SPM ordering

HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25

Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28, 2024 • 14
HyperCLOVA X Technical Report

Paper • 2404.01954 • Published Apr 2, 2024 • 25
Instruction Tuning with Human Curriculum

Paper • 2310.09518 • Published Oct 14, 2023 • 3

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