Domain-specific big language models have emerged attributable to the oversaturation of general large language models (LLMs). Three predominant categories could also be used to group existing methodologies. The primary builds models from scratch using a mixture of generic and domain-specific corpora. Although this naturally produces domain-specific LLMs, the massive computational and data needs cause serious issues. The second method, which is more economical, refines the language model using supervised datasets. Nonetheless, it must be determined how well-tuned LLMs can understand domain knowledge that might be utilized across all domain-specific activities. Within the third, recovered domain information is used to motivate the overall language model, which could also be seen as an application of LLM quite than a direct improvement to the LLM itself. 

Researchers from Microsoft try domain-adaptive pretraining, or ongoing pretraining on domain-specific corpora, which they consider is helpful in customizing different natural language processing models to certain domains. By combining domain-specific knowledge with broad ability, this method advantages downstream domain-specific activities while incurring less expense. This drives their research into whether ongoing pretraining is similarly advantageous for extensive generative models. They undertake preliminary experiments on three domains, biology, finance, and law, and find that further training on the raw corpora drastically reduces prompting performance while maintaining advantages for fine-tuning assessment and knowledge probing tests. This leads us to the conclusion that domain-adaptive pretraining using raw corpora teaches the LLM concerning the domain while impairing its capability to prompt. 

Figure 1 shows a condensed example of a reading comprehension text. The raw text is followed by a series of tasks which might be built from it, corresponding to summarization (purple), word-to-text (blue), natural language inference (red), common sense reasoning (teal), paraphrase detection (yellow), and text completion (green). 

They provide an easy approach for converting massive raw corpora into reading comprehension texts to make use of domain-specific knowledge and improve prompting performance. Each raw text is enhanced with several tasks pertinent to its topic, as shown in Figure 1. These exercises are intended to support the model’s continued capability to answer queries in natural language, depending on the context of the unique text. To further improve prompting ability, they supply a wide range of generic directions to the reading comprehension texts. Their tests in biology, economics, and law show how well their method enhances model performance on quite a few domain-specific tasks. They call the ultimate model, which stands for Adapted Large Language Model, AdaptLLM. In the longer term, they see this process expanded to incorporate making a generic big language model, adding to the ever-expanding canvas of jobs across additional domains. 

In conclusion, their contributions consist of: 

• Of their investigation of ongoing pretraining for giant language models, they find that while continuing to coach the model on domain-specific raw corpora can provide domain knowledge, it severely degrades its capability to prompt. 

• To efficiently learn the domain knowledge while concurrently maintaining prompting performance, they present an easy recipe that mechanically turns massive raw corpora into reading comprehension texts. Their tests show that their approach commonly enhances model performance in three distinct fields: biology, finance, and law.

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