Add to ORKGWith the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains. Nevertheless, there remains a demand for high-quality, domain-specific outputs in areas like healthcare, law, and finance. This paper first evaluates the existing large models for specialized domains and discusses their limitations. To cater to the specific needs of certain domains, we introduce the ``MiChao-HuaFen 1.0'' pre-trained corpus dataset, tailored for the news and governmental sectors. The dataset, sourced from publicly available internet data from 2022, underwent multiple rounds of cleansing and processing to ensure high quality and reliable origins, with provisions for ...
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on Co...
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal task...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...
As the demand for sophisticated Natural Language Processing (NLP) models continues to grow, so does ...
We present an empirical evaluation of various outputs generated by nine of the most widely-available...
Traditionally, large language models have been either trained on general web crawls or domain-specif...
This paper examines the comparative effectiveness of a specialized compiled language model and a gen...
Large language models (LLMs) have significantly advanced the field of natural language processing, w...
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled f...
While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capab...
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). Ho...
Realizing the recent advances in Natural Language Processing (NLP) to the legal sector poses challen...
Large language models (LLMs) are a special class of pretrained language models obtained by scaling m...
Domain Adaptation (DA) is a method for enhancing a model's performance on a target domain with inade...
The success of deep learning is largely due to the availability of large amounts of training data th...
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on Co...
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal task...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...
As the demand for sophisticated Natural Language Processing (NLP) models continues to grow, so does ...
We present an empirical evaluation of various outputs generated by nine of the most widely-available...
Traditionally, large language models have been either trained on general web crawls or domain-specif...
This paper examines the comparative effectiveness of a specialized compiled language model and a gen...
Large language models (LLMs) have significantly advanced the field of natural language processing, w...
Inspired by Federated Learning, in this paper, we propose personal large models that are distilled f...
While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capab...
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). Ho...
Realizing the recent advances in Natural Language Processing (NLP) to the legal sector poses challen...
Large language models (LLMs) are a special class of pretrained language models obtained by scaling m...
Domain Adaptation (DA) is a method for enhancing a model's performance on a target domain with inade...
The success of deep learning is largely due to the availability of large amounts of training data th...
We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on Co...
Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal task...
Machine learning models that can generalize to unseen domains are essential when applied in real-wor...