A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
- URL: http://arxiv.org/abs/2405.10936v1
- Date: Fri, 17 May 2024 17:47:39 GMT
- Title: A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
- Authors: Kaiyu Huang, Fengran Mo, Hongliang Li, You Li, Yuanchi Zhang, Weijian Yi, Yulong Mao, Jinchen Liu, Yuzhuang Xu, Jinan Xu, Jian-Yun Nie, Yang Liu,
- Abstract summary: The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing.
Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient.
This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
- Score: 48.314619377988436
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia and industry. To mitigate potential discrimination and enhance the overall usability and accessibility for diverse language user groups, it is important for the development of language-fair technology. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient, where a comprehensive survey to summarize recent approaches, developments, limitations, and potential solutions is desirable. To this end, we provide a survey with multiple perspectives on the utilization of LLMs in the multilingual scenario. We first rethink the transitions between previous and current research on pre-trained language models. Then we introduce several perspectives on the multilingualism of LLMs, including training and inference methods, model security, multi-domain with language culture, and usage of datasets. We also discuss the major challenges that arise in these aspects, along with possible solutions. Besides, we highlight future research directions that aim at further enhancing LLMs with multilingualism. The survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
Related papers
- Responsible Multilingual Large Language Models: A Survey of Development, Applications, and Societal Impact [5.803667039914564]
This work bridges the gap by providing an end-to-end framework for developing and deploying MLLMs in production environments.
Our findings reveal critical challenges in supporting linguistic diversity, with 88.38% of world languages categorized as low-resource.
This survey provides essential guidance for practitioners and researchers working to develop more inclusive and effective multilingual AI systems.
arXiv Detail & Related papers (2024-10-23T03:19:15Z) - 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) - LLM for Everyone: Representing the Underrepresented in Large Language Models [21.07409393578553]
This thesis aims to bridge the gap in NLP research and development by focusing on underrepresented languages.
A comprehensive evaluation of large language models (LLMs) is conducted to assess their capabilities in these languages.
The proposed solutions cover cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment.
arXiv Detail & Related papers (2024-09-20T20:53:22Z) - Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
Large Language Models (LLMs) have shown impressive language capabilities.
In this work, we investigate the spontaneous multilingual alignment improvement of LLMs.
We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
arXiv Detail & Related papers (2024-05-22T16:46:19Z) - Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers [81.47046536073682]
We present a review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.
We hope our work can provide the community with quick access and spur breakthrough research in MLLMs.
arXiv Detail & Related papers (2024-04-07T11:52:44Z) - A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias [5.104497013562654]
We present an overview of MLLMs, covering their evolution, key techniques, and multilingual capacities.
We explore widely utilized multilingual corpora for MLLMs' training and multilingual datasets oriented for downstream tasks.
We discuss bias on MLLMs including its category and evaluation metrics, and summarize the existing debiasing techniques.
arXiv Detail & Related papers (2024-04-01T05:13:56Z) - Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora.
We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs.
Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
arXiv Detail & Related papers (2024-02-26T09:36:05Z) - History, Development, and Principles of Large Language Models-An Introductory Survey [15.875687167037206]
Language models serve as a cornerstone in natural language processing (NLP)
Over extensive research spanning decades, language modeling has progressed from initial statistical language models (SLMs) to the contemporary landscape of large language models (LLMs)
arXiv Detail & Related papers (2024-02-10T01:18:15Z) - A Survey of Large Language Models [81.06947636926638]
Language modeling has been widely studied for language understanding and generation in the past two decades.
Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora.
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
arXiv Detail & Related papers (2023-03-31T17:28:46Z)
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.