GECKO: Generative Language Model for English, Code and Korean

Sungwoo Oh, Donggyu Kim·May 24, 2024

Summary

GECKO is a bilingual large language model designed for Korean, English, and programming languages, trained using the LLaMA architecture on a balanced corpus of 35% Korean, 28% English, and 37% code. It stands out for its efficient token generation in Korean and strong performance on the Korean MMLU benchmark, despite having a smaller vocabulary than English-focused models. The model's tokenizer, optimized for Korean and context-aware outputs, outperforms competitors like Polyglot-Ko and LLaMA-2. GECKO employs a decoder-only Transformer, AdamW optimizer, and sequence packing for training, and is open-source under a permissive license. The study compares GECKO with other models, emphasizing its strength in Korean understanding and moderate performance in English and coding tasks. The research also explores the benefits of open-source AI, model scaling, and the importance of diverse language models for academic research and practical applications. Future plans include improving the model with more training resources and instruction fine-tuning.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of developing a bilingual large language model (LLM) optimized for Korean and English, along with programming languages, known as GECKO . This paper introduces GECKO as an open-source model that contributes to academic research and practical development in the field of large language model pretraining . While the development of large language models is not a new problem, the specific focus on creating a bilingual LLM optimized for Korean and English, along with programming languages, represents a novel contribution to the field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the performance and capabilities of the GECKO language model in various domains such as knowledge and reasoning abilities, coding, mathematics, and Korean understanding . The validation involves assessing the model's performance against standard academic benchmarks for knowledge and reasoning abilities , as well as coding and mathematics . Additionally, the paper evaluates the model's performance in Korean understanding using the KMMLU evaluation set . The results indicate that GECKO demonstrates better performance in Korean understanding compared to other evaluated models and shows moderate performance in coding and mathematics .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces GECKO, a bilingual large language model optimized for Korean and English, as well as programming languages. GECKO is pretrained on a balanced corpus of Korean and English using the LLaMA architecture. The model aims to contribute to academic research and practical development by providing an open-source Korean pretrained LLM . One key aspect of the paper is the focus on building a better data pipeline for the corpus and training the model efficiently . GECKO demonstrates high efficiency in token generation for both Korean and English, despite having a smaller vocabulary size. It performs well on Korean benchmarks like KMMLU (Korean MMLU) and shows modest performance in English and Code tasks, even with fewer trained tokens compared to English-focused LLMs .

Furthermore, the paper discusses the importance of open-sourcing artificial intelligence technologies to create safer products, accelerate innovation, and expand markets. By releasing GECKO under a permissive license, the authors aim to provide a research baseline and practical insights for Korean LLM research . The model's availability to the open-source community encourages collaboration and further advancements in the field of large language models . Additionally, the paper mentions the use of Cloud TPUs provided by the TRC Team at Google Cloud, which significantly enhanced the research efforts . The GECKO language model introduces several key characteristics and advantages compared to previous methods outlined in the paper :

  • Efficiency in Token Generation: GECKO demonstrates high efficiency in token generation for both Korean and English, despite having a smaller vocabulary size. This efficiency is measured in comparison to other tokenizers, showcasing its effectiveness .
  • Performance on Benchmarks: GECKO exhibits great performance on the Korean MMLU benchmark and shows modest performance in English and Code tasks, even with fewer trained tokens compared to English-focused LLMs. This highlights its versatility and effectiveness across different languages and tasks .
  • Open-Source Availability: GECKO is available to the open-source community under a permissive license, aiming to provide a research baseline and practical insights for Korean LLM research. This open-access approach fosters collaboration and advancements in the field of large language models .
  • Data Processing Pipeline: The model utilizes a sophisticated data processing pipeline that focuses on mitigating harmful content, minimizing data memorization, and preserving structural information in the training corpus. This approach enhances the model's robustness, generalization, and performance when exposed to new data .
  • Pretraining Methodology: GECKO is pretrained from scratch using terabytes of textual data, including Korean, English, and programming languages. This approach leverages language-specific datasets at the pretraining phase, contributing to the model's strong understanding of non-English languages like Korean .
  • Language Alignment and Preprocessing: The model aligns languages between English and Korean during pretraining and utilizes a curated corpus with a focus on deduplication and cleaning of raw text. This meticulous preprocessing enhances the quality of the training data and contributes to the model's performance .
  • Tokenizer Design: GECKO's tokenizer is trained on a balanced corpus of Korean, English, and Code using the Byte Pair Encoding (BPE) algorithm. The model treats numbers as individual digits and segments unknown characters into bytes to avoid out-of-vocabulary issues. The tokenizer's design aims to balance computational efficiency and performance, considering the demands of larger vocabularies during inference .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and notable researchers exist in the field of language models and machine learning:

  • Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, and others worked on scaling language modeling with pathways .
  • Hyung Won Chung, Le Hou, Shayne Longpre, and others focused on scaling instruction-finetuned language models .
  • Theo Gutman-Solo and his team worked on solving quantitative reasoning problems with language models .
  • Jirabovonvisut, Potsawee Manakul, and others explored Thai large language models .
  • Yue Wang, Hung Le, Akhilesh Deepak Gotmare, and others delved into open code large language models for code understanding and generation .

The key to the solution mentioned in the paper involves several steps:

  1. Language Alignment: The model was trained to align languages between English and Korean using translation datasets .
  2. Preprocessing: Terabytes of Korean corpus were curated and processed, along with open-source corpora for English and programming languages. Data cleansing involved deduplication, removal of harmful content, minimizing data memorization, and preserving structural information .
  3. Pretraining: The GECKO tokenizer was trained on a balanced corpus of Korean, English, and Code using the Byte Pair Encoding (BPE) algorithm. The total vocabulary size was set to 32,000 to balance computational efficiency and performance .

How were the experiments in the paper designed?

The experiments in the paper were designed by introducing GECKO, a bilingual large language model optimized for Korean and English, along with programming languages. GECKO was pretrained on a balanced, high-quality corpus of Korean and English using the LLaMA architecture. The model's performance was evaluated on representative benchmarks for Korean, English, and Code, showcasing great efficiency in token generations for both Korean and English despite its smaller vocabulary size. The experiments aimed to measure GECKO's performance on tasks such as KMMLU (Korean MMLU) and assess its modest performance in English and Code, even with fewer trained tokens compared to English-focused LLMs .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the GECKO project is the KMMLU dataset . The code for GECKO is open source, as it is mentioned that GECKO is an open-source Korean pretrained LLM released under a permissive license .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted evaluations using standard academic benchmarks to assess knowledge, reasoning abilities, coding, mathematics, and Korean understanding . The findings demonstrated that the GECKO model exhibited better performance in Korean understanding compared to other evaluated models . Additionally, the paper outlined the methodology for preprocessing data, training the model, and tokenization, which are crucial steps in building and evaluating language models . These detailed processes contribute to the credibility and reliability of the experimental results presented in the paper.


What are the contributions of this paper?

The paper "GECKO: Generative Language Model for English, Code and Korean" makes several contributions:

  • It introduces GECKO, an open-source Korean pretrained LLM released under a permissive license, which can benefit academic research and practical development of large Korean language models .
  • The authors aim to release an improved version of the model with additional training resources and are preparing for instruction fine-tuning to evaluate GECKO’s instruction-following ability .
  • The paper emphasizes the importance of open-sourcing artificial intelligence technologies to create safer products, accelerate innovation, and expand markets .

What work can be continued in depth?

Further research can be conducted to enhance pretraining methods and applications for Korean Large Language Models (LLMs) like GECKO. Despite previous achievements in Korean language models, there is still limited research on pretraining methods and applications for Korean LLMs . This suggests an opportunity to delve deeper into developing and refining pretraining strategies specifically tailored for Korean language models to improve their performance and capabilities . Additionally, exploring the potential for instruction fine-tuning to evaluate the instruction-following ability of models like GECKO could be a valuable area for further investigation and development .


Introduction
Background
Multilingual Model Landscape
The Need for Korean-focused Models
Objective
Primary Goal: GECKO's Performance in Korean and Programming
Supporting Objective: Open-Source AI and Model Scaling
Methodology
Data Collection
Corpus Composition
Korean (35%)
English (28%)
Programming Languages (37%)
Data Source and Diversity
Data Preprocessing
Tokenizer Design
Korean-specific optimization
Context-awareness
Comparison with Competitors
Polyglot-Ko and LLaMA-2
Model Architecture
Decoder-Only Transformer
AdamW Optimizer and Sequence Packing
Training Process
Training Techniques
Performance Metrics
Model Evaluation
Korean MMLU Benchmark
Strength in Korean Language Understanding
Comparison with English-focused Models
English and Coding Tasks
Moderate Performance
Real-World Applications
Open-Source Impact
Benefits and Transparency
Community Collaboration
Future Directions
Scaling and Resource Allocation
Instruction Fine-Tuning
Potential Improvements
Conclusion
The Importance of Diverse Language Models
GECKO's Role in Academic and Practical Research
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the key factors that contribute to GECKO's performance in the Korean MMLU benchmark?
What is the primary focus of the GECKO language model?
How does GECKO compare to English-focused models in terms of its training data distribution?
What makes GECKO's tokenizer unique and advantageous in Korean language processing?

GECKO: Generative Language Model for English, Code and Korean

Sungwoo Oh, Donggyu Kim·May 24, 2024

Summary

GECKO is a bilingual large language model designed for Korean, English, and programming languages, trained using the LLaMA architecture on a balanced corpus of 35% Korean, 28% English, and 37% code. It stands out for its efficient token generation in Korean and strong performance on the Korean MMLU benchmark, despite having a smaller vocabulary than English-focused models. The model's tokenizer, optimized for Korean and context-aware outputs, outperforms competitors like Polyglot-Ko and LLaMA-2. GECKO employs a decoder-only Transformer, AdamW optimizer, and sequence packing for training, and is open-source under a permissive license. The study compares GECKO with other models, emphasizing its strength in Korean understanding and moderate performance in English and coding tasks. The research also explores the benefits of open-source AI, model scaling, and the importance of diverse language models for academic research and practical applications. Future plans include improving the model with more training resources and instruction fine-tuning.
Mind map
Context-awareness
Korean-specific optimization
Programming Languages (37%)
English (28%)
Korean (35%)
Community Collaboration
Benefits and Transparency
Real-World Applications
Moderate Performance
Comparison with English-focused Models
Strength in Korean Language Understanding
Performance Metrics
Training Techniques
AdamW Optimizer and Sequence Packing
Decoder-Only Transformer
Polyglot-Ko and LLaMA-2
Comparison with Competitors
Tokenizer Design
Data Source and Diversity
Corpus Composition
Supporting Objective: Open-Source AI and Model Scaling
Primary Goal: GECKO's Performance in Korean and Programming
The Need for Korean-focused Models
Multilingual Model Landscape
GECKO's Role in Academic and Practical Research
The Importance of Diverse Language Models
Potential Improvements
Instruction Fine-Tuning
Scaling and Resource Allocation
Open-Source Impact
English and Coding Tasks
Korean MMLU Benchmark
Training Process
Model Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Directions
Model Evaluation
Methodology
Introduction
Outline
Introduction
Background
Multilingual Model Landscape
The Need for Korean-focused Models
Objective
Primary Goal: GECKO's Performance in Korean and Programming
Supporting Objective: Open-Source AI and Model Scaling
Methodology
Data Collection
Corpus Composition
Korean (35%)
English (28%)
Programming Languages (37%)
Data Source and Diversity
Data Preprocessing
Tokenizer Design
Korean-specific optimization
Context-awareness
Comparison with Competitors
Polyglot-Ko and LLaMA-2
Model Architecture
Decoder-Only Transformer
AdamW Optimizer and Sequence Packing
Training Process
Training Techniques
Performance Metrics
Model Evaluation
Korean MMLU Benchmark
Strength in Korean Language Understanding
Comparison with English-focused Models
English and Coding Tasks
Moderate Performance
Real-World Applications
Open-Source Impact
Benefits and Transparency
Community Collaboration
Future Directions
Scaling and Resource Allocation
Instruction Fine-Tuning
Potential Improvements
Conclusion
The Importance of Diverse Language Models
GECKO's Role in Academic and Practical Research

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of developing a bilingual large language model (LLM) optimized for Korean and English, along with programming languages, known as GECKO . This paper introduces GECKO as an open-source model that contributes to academic research and practical development in the field of large language model pretraining . While the development of large language models is not a new problem, the specific focus on creating a bilingual LLM optimized for Korean and English, along with programming languages, represents a novel contribution to the field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the performance and capabilities of the GECKO language model in various domains such as knowledge and reasoning abilities, coding, mathematics, and Korean understanding . The validation involves assessing the model's performance against standard academic benchmarks for knowledge and reasoning abilities , as well as coding and mathematics . Additionally, the paper evaluates the model's performance in Korean understanding using the KMMLU evaluation set . The results indicate that GECKO demonstrates better performance in Korean understanding compared to other evaluated models and shows moderate performance in coding and mathematics .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces GECKO, a bilingual large language model optimized for Korean and English, as well as programming languages. GECKO is pretrained on a balanced corpus of Korean and English using the LLaMA architecture. The model aims to contribute to academic research and practical development by providing an open-source Korean pretrained LLM . One key aspect of the paper is the focus on building a better data pipeline for the corpus and training the model efficiently . GECKO demonstrates high efficiency in token generation for both Korean and English, despite having a smaller vocabulary size. It performs well on Korean benchmarks like KMMLU (Korean MMLU) and shows modest performance in English and Code tasks, even with fewer trained tokens compared to English-focused LLMs .

Furthermore, the paper discusses the importance of open-sourcing artificial intelligence technologies to create safer products, accelerate innovation, and expand markets. By releasing GECKO under a permissive license, the authors aim to provide a research baseline and practical insights for Korean LLM research . The model's availability to the open-source community encourages collaboration and further advancements in the field of large language models . Additionally, the paper mentions the use of Cloud TPUs provided by the TRC Team at Google Cloud, which significantly enhanced the research efforts . The GECKO language model introduces several key characteristics and advantages compared to previous methods outlined in the paper :

  • Efficiency in Token Generation: GECKO demonstrates high efficiency in token generation for both Korean and English, despite having a smaller vocabulary size. This efficiency is measured in comparison to other tokenizers, showcasing its effectiveness .
  • Performance on Benchmarks: GECKO exhibits great performance on the Korean MMLU benchmark and shows modest performance in English and Code tasks, even with fewer trained tokens compared to English-focused LLMs. This highlights its versatility and effectiveness across different languages and tasks .
  • Open-Source Availability: GECKO is available to the open-source community under a permissive license, aiming to provide a research baseline and practical insights for Korean LLM research. This open-access approach fosters collaboration and advancements in the field of large language models .
  • Data Processing Pipeline: The model utilizes a sophisticated data processing pipeline that focuses on mitigating harmful content, minimizing data memorization, and preserving structural information in the training corpus. This approach enhances the model's robustness, generalization, and performance when exposed to new data .
  • Pretraining Methodology: GECKO is pretrained from scratch using terabytes of textual data, including Korean, English, and programming languages. This approach leverages language-specific datasets at the pretraining phase, contributing to the model's strong understanding of non-English languages like Korean .
  • Language Alignment and Preprocessing: The model aligns languages between English and Korean during pretraining and utilizes a curated corpus with a focus on deduplication and cleaning of raw text. This meticulous preprocessing enhances the quality of the training data and contributes to the model's performance .
  • Tokenizer Design: GECKO's tokenizer is trained on a balanced corpus of Korean, English, and Code using the Byte Pair Encoding (BPE) algorithm. The model treats numbers as individual digits and segments unknown characters into bytes to avoid out-of-vocabulary issues. The tokenizer's design aims to balance computational efficiency and performance, considering the demands of larger vocabularies during inference .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers and notable researchers exist in the field of language models and machine learning:

  • Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, and others worked on scaling language modeling with pathways .
  • Hyung Won Chung, Le Hou, Shayne Longpre, and others focused on scaling instruction-finetuned language models .
  • Theo Gutman-Solo and his team worked on solving quantitative reasoning problems with language models .
  • Jirabovonvisut, Potsawee Manakul, and others explored Thai large language models .
  • Yue Wang, Hung Le, Akhilesh Deepak Gotmare, and others delved into open code large language models for code understanding and generation .

The key to the solution mentioned in the paper involves several steps:

  1. Language Alignment: The model was trained to align languages between English and Korean using translation datasets .
  2. Preprocessing: Terabytes of Korean corpus were curated and processed, along with open-source corpora for English and programming languages. Data cleansing involved deduplication, removal of harmful content, minimizing data memorization, and preserving structural information .
  3. Pretraining: The GECKO tokenizer was trained on a balanced corpus of Korean, English, and Code using the Byte Pair Encoding (BPE) algorithm. The total vocabulary size was set to 32,000 to balance computational efficiency and performance .

How were the experiments in the paper designed?

The experiments in the paper were designed by introducing GECKO, a bilingual large language model optimized for Korean and English, along with programming languages. GECKO was pretrained on a balanced, high-quality corpus of Korean and English using the LLaMA architecture. The model's performance was evaluated on representative benchmarks for Korean, English, and Code, showcasing great efficiency in token generations for both Korean and English despite its smaller vocabulary size. The experiments aimed to measure GECKO's performance on tasks such as KMMLU (Korean MMLU) and assess its modest performance in English and Code, even with fewer trained tokens compared to English-focused LLMs .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the GECKO project is the KMMLU dataset . The code for GECKO is open source, as it is mentioned that GECKO is an open-source Korean pretrained LLM released under a permissive license .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted evaluations using standard academic benchmarks to assess knowledge, reasoning abilities, coding, mathematics, and Korean understanding . The findings demonstrated that the GECKO model exhibited better performance in Korean understanding compared to other evaluated models . Additionally, the paper outlined the methodology for preprocessing data, training the model, and tokenization, which are crucial steps in building and evaluating language models . These detailed processes contribute to the credibility and reliability of the experimental results presented in the paper.


What are the contributions of this paper?

The paper "GECKO: Generative Language Model for English, Code and Korean" makes several contributions:

  • It introduces GECKO, an open-source Korean pretrained LLM released under a permissive license, which can benefit academic research and practical development of large Korean language models .
  • The authors aim to release an improved version of the model with additional training resources and are preparing for instruction fine-tuning to evaluate GECKO’s instruction-following ability .
  • The paper emphasizes the importance of open-sourcing artificial intelligence technologies to create safer products, accelerate innovation, and expand markets .

What work can be continued in depth?

Further research can be conducted to enhance pretraining methods and applications for Korean Large Language Models (LLMs) like GECKO. Despite previous achievements in Korean language models, there is still limited research on pretraining methods and applications for Korean LLMs . This suggests an opportunity to delve deeper into developing and refining pretraining strategies specifically tailored for Korean language models to improve their performance and capabilities . Additionally, exploring the potential for instruction fine-tuning to evaluate the instruction-following ability of models like GECKO could be a valuable area for further investigation and development .

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