SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages

• URL: http://arxiv.org/abs/2407.19672v1
• Date: Mon, 29 Jul 2024 03:26:22 GMT
• Title: SeaLLMs 3: Open Foundation and Chat Multilingual Large Language Models for Southeast Asian Languages
• Authors: Wenxuan Zhang, Hou Pong Chan, Yiran Zhao, Mahani Aljunied, Jianyu Wang, Chaoqun Liu, Yue Deng, Zhiqiang Hu, Weiwen Xu, Yew Ken Chia, Xin Li, Lidong Bing,
• Abstract summary: 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.
• Score: 77.75535024869224
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) have shown remarkable abilities across various tasks, yet their development has predominantly centered on high-resource languages like English and Chinese, leaving low-resource languages underserved. To address this disparity, we present SeaLLMs 3, the latest iteration of the SeaLLMs model family, tailored for Southeast Asian languages. This region, characterized by its rich linguistic diversity, has lacked adequate language technology support. 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. Leveraging efficient language enhancement techniques and a specially constructed instruction tuning dataset, SeaLLMs 3 significantly reduces training costs while maintaining high performance and versatility. 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. Additionally, we prioritized safety and reliability by addressing both general and culture-specific considerations and incorporated mechanisms to reduce hallucinations. This work underscores the importance of inclusive AI, showing that advanced LLM capabilities can benefit underserved linguistic and cultural communities.

Related papers

• Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages [55.36534539177367]
  This paper introduces Pangea, a multilingual multimodal large language model (MLLM) trained on a diverse 6M instruction dataset spanning 39 languages. P Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts. We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs.
  arXiv  Detail & Related papers  (2024-10-21T16:19:41Z)
• Lens: Rethinking Multilingual Enhancement for Large Language Models [70.85065197789639]
  Lens is a novel approach to enhance multilingual capabilities of large language models (LLMs) It operates by manipulating the hidden representations within the language-agnostic and language-specific subspaces from top layers of LLMs. It achieves superior results with much fewer computational resources compared to existing post-training approaches.
  arXiv  Detail & Related papers  (2024-10-06T08:51:30Z)
• Breaking Boundaries: Investigating the Effects of Model Editing on Cross-linguistic Performance [6.907734681124986]
  This paper strategically identifies the need for linguistic equity by examining several knowledge editing techniques in multilingual contexts. We evaluate the performance of models such as Mistral, TowerInstruct, OpenHathi, Tamil-Llama, and Kan-Llama across languages including English, German, French, Italian, Spanish, Hindi, Tamil, and Kannada.
  arXiv  Detail & Related papers  (2024-06-17T01:54:27Z)
• SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages [64.10040374077994]
  We introduce SEACrowd, a collaborative initiative that consolidates standardized corpora in nearly 1,000 languages across three modalities. We assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA.
  arXiv  Detail & Related papers  (2024-06-14T15:23:39Z)
• Compass: Large Multilingual Language Model for South-east Asia [0.0]
  CompassLLM is a large multilingual model specifically tailored for Southeast Asian languages. Our model exhibits its superior performance in South-east Asia languages, such as Indonesian language.
  arXiv  Detail & Related papers  (2024-04-14T11:48:33Z)
• Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages [55.963648108438555]
  Large language models (LLMs) show remarkable human-like capability in various domains and languages. We introduce Cendol, a collection of Indonesian LLMs encompassing both decoder-only and encoder-decoder architectures. We highlight Cendol's effectiveness across a diverse array of tasks, attaining 20% improvement, and demonstrate its capability to generalize.
  arXiv  Detail & Related papers  (2024-04-09T09:04:30Z)
• SeaLLMs -- Large Language Models for Southeast Asia [76.50157503379086]
  We introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages. SeaLLMs are built upon the Llama-2 model and further advanced through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning. Our comprehensive evaluation demonstrates that SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities.
  arXiv  Detail & Related papers  (2023-12-01T17:17:56Z)
• BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models [0.06597195879147556]
  BHASA is a holistic linguistic and cultural evaluation suite for Large Language Models (LLMs) in Southeast Asian languages. It comprises three components: (1) a NLP benchmark covering eight tasks across Natural Language Understanding (NLU), Generation (NLG) and Reasoning (NLR) tasks, (2) LINDSEA, a linguistic diagnostic toolkit that spans the gamut of linguistic phenomena including syntax, semantics and pragmatics, and (3) a cultural diagnostics dataset that probes for both cultural representation and sensitivity.
  arXiv  Detail & Related papers  (2023-09-12T09:31:25Z)

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.