Summary of Bridging the Bosphorus: Advancing Turkish Large Language Models Through Strategies For Low-resource Language Adaptation and Benchmarking, by Emre Can Acikgoz et al.
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking
by Emre Can Acikgoz, Mete Erdogan, Deniz Yuret
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) are crucial across various fields, emphasizing the need for high-quality models in underrepresented languages. This study explores the unique challenges faced by low-resource languages like Turkish, including data scarcity, model selection, evaluation, and computational limitations. The authors evaluate the impact of training strategies, model choices, and data availability on LLM performance designed for underrepresented languages. Two methodologies are employed: adapting existing LLMs to Turkish or developing a model from scratch using Turkish pretraining data, both with supervised fine-tuning on a novel Turkish instruction-tuning dataset. Performance is evaluated through a new leaderboard for Turkish LLMs, featuring benchmarks assessing reasoning and knowledge skills. The study also explores data and model scaling during pretraining and fine-tuning, emphasizing knowledge transfer across languages and addressing catastrophic forgetting during fine-tuning on a different language. The authors aim to provide a detailed guide for advancing the LLM framework in low-resource linguistic contexts, making NLP benefits more globally accessible. Key contributions include a novel Turkish instruction-tuning dataset, an evaluation methodology for Turkish LLMs, and insights into data and model scaling strategies for underrepresented languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about building language models that can understand languages that don’t have much information available online. The authors looked at the challenges of building these models, like not having enough data or knowing which type of model to use. They tested different approaches to see what works best for a language called Turkish. They also created a new way to test how well these models work and found some strategies that help improve their performance. The goal is to make it easier to build language models for languages that don’t have as much information available, so more people can use natural language processing (NLP) technology worldwide. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Natural language processing » Nlp » Pretraining » Supervised
