LaVy: Vietnamese Multimodal Large Language Model

• URL: http://arxiv.org/abs/2404.07922v6
• Date: Tue, 16 Jul 2024 06:37:43 GMT
• Title: LaVy: Vietnamese Multimodal Large Language Model
• Authors: Chi Tran, Huong Le Thanh,
• Abstract summary: Large Language Models (LLMs) and Multimodal Large language models (MLLMs) have taken the world by storm with impressive abilities in complex reasoning and linguistic comprehension. There are plethora of works related to Vietnamese Large Language Models, the lack of high-quality resources in multimodality limits the progress of Vietnamese MLLMs. In this paper, we pioneer in address this by introducing LaVy, a state-of-the-art Vietnamese MLLM, and we also introduce LaVy-Bench benchmark designated for evaluating MLLMs's understanding on Vietnamese visual language tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) and Multimodal Large language models (MLLMs) have taken the world by storm with impressive abilities in complex reasoning and linguistic comprehension. Meanwhile there are plethora of works related to Vietnamese Large Language Models, the lack of high-quality resources in multimodality limits the progress of Vietnamese MLLMs. In this paper, we pioneer in address this by introducing LaVy, a state-of-the-art Vietnamese MLLM, and we also introduce LaVy-Bench benchmark designated for evaluating MLLMs's understanding on Vietnamese visual language tasks. Our project is public at https://github.com/baochi0212/LaVy

Related papers

• A Survey on Benchmarks of Multimodal Large Language Models [65.87641718350639]
  This paper presents a comprehensive review of 200 benchmarks and evaluations for Multimodal Large Language Models (MLLMs) We focus on (1)perception and understanding, (2)cognition and reasoning, (3)specific domains, (4)key capabilities, and (5)other modalities. Our key argument is that evaluation should be regarded as a crucial discipline to support the development of MLLMs better.
  arXiv  Detail & Related papers  (2024-08-16T09:52:02Z)
• SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages [77.75535024869224]
  We present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. SeaLLMs 3 aims to bridge this gap by covering a comprehensive range of languages spoken in this region, including English, Chinese, Indonesian, Vietnamese, Thai, Tagalog, Malay, Burmese, Khmer, Lao, Tamil, and Javanese. Our model excels in tasks such as world knowledge, mathematical reasoning, translation, and instruction following, achieving state-of-the-art performance among similarly sized models.
  arXiv  Detail & Related papers  (2024-07-29T03:26:22Z)
• ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models [0.0]
  ViLLM-Eval is a comprehensive evaluation suite designed to measure the advanced knowledge and reasoning abilities of foundation models. A thorough evaluation of the most advanced LLMs on ViLLM-Eval revealed that even the best performing models have significant room for improvement.
  arXiv  Detail & Related papers  (2024-04-17T05:57:17Z)
• Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training [0.0]
  vi-mistral-x is an innovative Large Language Model designed specifically for the Vietnamese language. It utilizes a unique method of continual pre-training, based on the Mistral architecture. It has shown to outperform existing Vietnamese LLMs in several key areas, including text classification, question answering, and text generation.
  arXiv  Detail & Related papers  (2024-03-20T10:14:13Z)
• Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models [25.724995114710165]
  We investigate the design aspects of Multimodal Small Language Models (MSLMs) and propose an efficient multimodal assistant named Mipha. Our Mipha-3B outperforms the state-of-the-art large MLLMs, especially LLaVA-1.5-13B, on multiple benchmarks.
  arXiv  Detail & Related papers  (2024-03-10T12:43:27Z)
• VinaLLaMA: LLaMA-based Vietnamese Foundation Model [4.531874270358511]
  VinaLLaMA is an open-weight, state-of-the-art (SOTA) Large Language Model for the Vietnamese language, built upon LLaMA-2 with an additional 800 billion trained tokens. VinaLLaMA-7B-chat, trained on 1 million high-quality synthetic samples, achieves SOTA results on key benchmarks, including VLSP, VMLU, and Vicuna Benchmark Vietnamese.
  arXiv  Detail & Related papers  (2023-12-18T08:27:33Z)
• ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing [1.1765925931670576]
  We present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks.
  arXiv  Detail & Related papers  (2023-10-17T11:34:50Z)
• Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages [76.35234803589412]
  MPM is an effective training paradigm for training large multimodal models in non-English languages. We build large multimodal models VisCPM in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese.
  arXiv  Detail & Related papers  (2023-08-23T09:55:41Z)
• Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback [61.83548032416181]
  We present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
  arXiv  Detail & Related papers  (2023-07-29T18:01:46Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot. This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)

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.