RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining

• URL: http://arxiv.org/abs/2408.11294v1
• Date: Wed, 21 Aug 2024 02:49:41 GMT
• Title: RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining
• Authors: Anh-Dung Vo, Minseong Jung, Wonbeen Lee, Daewoo Choi,
• Abstract summary: We present RedWhale, a model specifically tailored for Korean language processing. RedWhale is developed using an efficient continual pretraining approach that includes a comprehensive Korean corpus preprocessing pipeline. Experimental results demonstrate that RedWhale outperforms other leading models on Korean NLP benchmarks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of Natural Language Processing (NLP) has seen significant advancements with the development of Large Language Models (LLMs). However, much of this research remains focused on English, often overlooking low-resource languages like Korean. This oversight presents challenges due to the unique non-alphabetic token structure of Korean and the substantial memory and computational demands required for LLM training, which frequently lead to memory constraints and out-of-memory errors. To address these issues, we present RedWhale, a model specifically tailored for Korean language processing. RedWhale is developed using an efficient continual pretraining approach that includes a comprehensive Korean corpus preprocessing pipeline, a specialized tokenizer, an optimized model initialization technique, and a multistage pretraining strategy. These innovations collectively reduce training time and computational costs while maintaining high levels of accuracy and comprehension. By leveraging cross-lingual transfer learning, which exploits shared linguistic similarities across languages, RedWhale builds on English models to enhance Korean language processing. Experimental results demonstrate that RedWhale outperforms other leading models on Korean NLP benchmarks, including the Korean Balanced Evaluation of Significant Tasks (KoBEST), showing superior understanding and generation of Korean text. Furthermore, RedWhale showed no signs of convergence even after pretraining on 9.7 billion tokens, indicating the potential for further improvements with additional training. This work represents a significant advancement in bridging the linguistic divide, particularly in enhancing NLP capabilities for the Korean language.

Related papers

• InstructionCP: A fast approach to transfer Large Language Models into target language [55.2480439325792]
  InsCP integrates instruction tags into the CP process to prevent loss of conversational proficiency while acquiring new languages. Our experiments demonstrate that InsCP retains conversational and Reinforcement Learning from Human Feedback abilities. This approach requires only 0.1 billion tokens of high-quality instruction-following data, thereby reducing resource consumption.
  arXiv  Detail & Related papers  (2024-05-30T15:45:13Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• HyperCLOVA X Technical Report [119.94633129762133]
  We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English.
  arXiv  Detail & Related papers  (2024-04-02T13:48:49Z)
• Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models [9.359647125218359]
  This report introduces textttEEVE-Korean-v1.0, a Korean adaptation of large language models. Our method can significantly boost non-English proficiency within just 2 billion tokens.
  arXiv  Detail & Related papers  (2024-02-22T17:12:39Z)
• HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models [0.0]
  We introduce the HAE-RAE Bench, a dataset curated to challenge models lacking Korean cultural and contextual depth. The dataset encompasses six downstream tasks across four domains: vocabulary, history, general knowledge, and reading comprehension.
  arXiv  Detail & Related papers  (2023-09-06T04:38:16Z)
• Improving Language Plasticity via Pretraining with Active Forgetting [63.36484652568976]
  We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Experiments with RoBERTa show that models pretrained with our forgetting mechanism demonstrate faster convergence during language adaptation.
  arXiv  Detail & Related papers  (2023-07-03T17:12:44Z)
• KOBEST: Korean Balanced Evaluation of Significant Tasks [3.664687661363732]
  A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field. We propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks.
  arXiv  Detail & Related papers  (2022-04-09T20:13:51Z)
• Cross-lingual Transferring of Pre-trained Contextualized Language Models [73.97131976850424]
  We propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate TRILayer" structure. We show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency.
  arXiv  Detail & Related papers  (2021-07-27T06:51:13Z)
• Token-wise Curriculum Learning for Neural Machine Translation [94.93133801641707]
  Existing curriculum learning approaches to Neural Machine Translation (NMT) require sufficient sampling amounts of "easy" samples from training data at the early training stage. We propose a novel token-wise curriculum learning approach that creates sufficient amounts of easy samples. Our approach can consistently outperform baselines on 5 language pairs, especially for low-resource languages.
  arXiv  Detail & Related papers  (2021-03-20T03:57:59Z)
• KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding [6.414554168135807]
  KoreALBERT is a monolingual ALBERT model specifically for Korean language understanding. Our pretrained KoreALBERT outperforms its BERT counterpart on 6 different NLU tasks.
  arXiv  Detail & Related papers  (2021-01-27T12:48:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.