Summary of Vbart: the Turkish Llm, by Meliksah Turker et al.
VBART: The Turkish LLM
by Meliksah Turker, Mehmet Erdi Ari, Aydin Han
First submitted to arxiv on: 2 Mar 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 The paper presents VBART, a sequence-to-sequence Large Language Model (LLM) pre-trained on a large Turkish corpus. VBART is based on the BART and mBART models and comes in two sizes: Large and XLarge. Fine-tuned VBART models outperform previous state-of-the-art results in tasks such as abstractive text summarization, title generation, text paraphrasing, question answering, and question generation. The models can be fine-tuned for future text generation tasks and datasets, opening up new avenues for Turkish Natural Language Processing (NLP) research. VBART surpasses multilingual models by up to 3x in certain tasks, improving existing results and providing efficient models for training and inference. Additionally, the paper introduces a monolingual tokenizer that is up to 11x more efficient than multilingual tokenizers. The authors also propose a method to enlarge an existing pre-trained LLM and question the relevance of Chinchilla Scaling Law to sequence-to-sequence masked language models. The fine-tuned VBART models, tokenizer, and cleaned vngrs-web-corpus (135 GB) are publicly available at this URL: http://huggingface.co/vngrs-ai. Overall, the paper presents a significant advancement in Turkish NLP research, demonstrating the potential of pre-trained LLMs for various text generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VBART is a new type of language model that’s specifically designed to work with Turkish text. It’s based on other successful models called BART and mBART, but it’s been trained only on Turkish data. This makes it much better at handling Turkish than other models that were trained on lots of different languages. The researchers used VBART to improve the results in several different tasks, such as summarizing text, generating titles, and answering questions. They found that VBART did a lot better than previous models in these tasks, and it was able to do this without needing to be re-trained for each specific task. One of the most interesting things about VBART is that it’s much more efficient than other models. This means that it can process large amounts of Turkish text quickly and accurately, which is important for lots of applications like chatbots and language translation systems. The researchers have made all of their data and code available online, so that others can use and build on their work. |
Keywords
* Artificial intelligence * Inference * Language model * Large language model * Natural language processing * Nlp * Question answering * Summarization * Text generation * Tokenizer * Translation
