Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain

• URL: http://arxiv.org/abs/2404.08262v3
• Date: Wed, 06 Nov 2024 16:19:24 GMT
• Title: Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain
• Authors: Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki,
• Abstract summary: This paper presents our findings from training and evaluating a Japanese business domain-specific LLM. Our pretrained model and business domain benchmark are publicly available to support further studies.
• Score: 4.133477882188227
• License:
• Abstract: The development of Large Language Models (LLMs) in various languages has been advancing, but the combination of non-English languages with domain-specific contexts remains underexplored. This paper presents our findings from training and evaluating a Japanese business domain-specific LLM designed to better understand business-related documents, such as the news on current affairs, technical reports, and patents. Additionally, LLMs in this domain require regular updates to incorporate the most recent knowledge. Therefore, we also report our findings from the first experiments and evaluations involving updates to this LLM using the latest article data, which is an important problem setting that has not been addressed in previous research. From our experiments on a newly created benchmark dataset for question answering in the target domain, we found that (1) our pretrained model improves QA accuracy without losing general knowledge, and (2) a proper mixture of the latest and older texts in the training data for the update is necessary. Our pretrained model and business domain benchmark are publicly available to support further studies.

Related papers

• TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text [5.523385345486362]
  We have developed language models specifically designed for legal applications. Our innovative approach significantly improves capabilities in legal tasks by using Large Language Models (LLMs) to convert raw training data into reading comprehension text.
  arXiv  Detail & Related papers  (2024-10-28T19:32:18Z)
• Exploring Language Model Generalization in Low-Resource Extractive QA [57.14068405860034]
  We investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift. We devise a series of experiments to empirically explain the performance gap.
  arXiv  Detail & Related papers  (2024-09-27T05:06:43Z)
• 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)
• BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models [56.89958793648104]
  Large Language Models (LLMs) are versatile and capable of addressing a diverse range of tasks. Previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. We present a novel framework named BLADE, which enhances Black-box LArge language models with small Domain-spEcific models.
  arXiv  Detail & Related papers  (2024-03-27T08:57:21Z)
• Investigating Continual Pretraining in Large Language Models: Insights and Implications [9.591223887442704]
  This paper studies the evolving domain of Continual Learning in large language models (LLMs) Our primary emphasis is on continual domain-adaptive pretraining, a process designed to equip LLMs with the ability to integrate new information from various domains. We examine the impact of model size on learning efficacy and forgetting, as well as how the progression and similarity of emerging domains affect the knowledge transfer within these models.
  arXiv  Detail & Related papers  (2024-02-27T10:47:24Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest [62.63606958140248]
  Large Language Models (LLMs) often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources. We train a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents.
  arXiv  Detail & Related papers  (2023-11-17T16:09:10Z)
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) They provide a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
  arXiv  Detail & Related papers  (2023-05-30T03:00:30Z)
• Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora [31.136334214818305]
  We study a lifelong language model pretraining challenge where a PTLM is continually updated so as to adapt to emerging data. Over a domain-incremental research paper stream and a chronologically ordered tweet stream, we incrementally pretrain a PTLM with different continual learning algorithms. Our experiments show continual learning algorithms improve knowledge preservation, with logit distillation being the most effective approach.
  arXiv  Detail & Related papers  (2021-10-16T09:59:33Z)

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.