Summary of Gecko: Generative Language Model For English, Code and Korean, by Sungwoo Oh and Donggyu Kim
GECKO: Generative Language Model for English, Code and Korean
by Sungwoo Oh, Donggyu Kim
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A bilingual large language model called GECKO is introduced, optimized for Korean and English, as well as programming languages. Pre-trained on a balanced corpus using the LLaMA architecture, GECKO exhibits efficiency in token generation for both languages despite its relatively small vocabulary size. Benchmark results show great performance on KMMLU (Korean MMLU) and modest performance in English and Code, outperforming smaller English-focused models. GECKO is available under an open-source license, serving as a research baseline and providing practical insights for Korean LLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GECKO is a special kind of computer program that can understand and generate text in both Korean and English languages. It’s like a super smart translator! Researchers created this model to help computers better understand human language, especially for tasks related to programming. They trained GECKO on a big collection of texts from Korea and the United States, using a special way of teaching called LLaMA architecture. The results show that GECKO is really good at generating text in Korean and English, even with its smaller vocabulary compared to other language models. This is an important step forward for artificial intelligence research. |
Keywords
» Artificial intelligence » Large language model » Llama » Token
