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Is Overnight Self-Training and Continuous Improvement the Future of Large Language Models?

1/15/24

Editorial team at Bits with Brains

The enhancement of large language models (LLMs) is making rapid and significant strides. One of the most exciting and recent developments is the concept of overnight self-updating LLMs.

Imagine going to sleep and instructing your LLM to update itself on a specific topic, such as a particular branch of medicine or a financial market trend. By morning, the LLM will have generated a smaller, highly focused model that can be used on a device like a phone.


This feat is achieved through an iterative self-training process that allows the LLM to continuously improve its performance on specific tasks. The LLM generates responses, evaluates these responses, and then refines its approach based on the evaluation.


A key aspect of this process is the integration of new knowledge into the LLM. The LLM searches for new data on the internet or other databases, and then reformulates this data into a format that can be used for in-context learning. This allows the LLM to stay up to date with the latest information and trends.


To facilitate reasoning and action on external data, the LLM employs ReAct-style agents. These agents can search for new data, evaluating the quality of this data, and then using it to refine the LLM's responses. This ensures that the LLM is always making the most informed decisions possible.


The LLM also uses a reinforcement self-training process for self-improvement. This involves generating responses, evaluating these responses, and then refining its approach based on the evaluation. This iterative process allows the LLM to continuously improve its performance on specific tasks.


To fine-tune the LLM, synthetic datasets are generated based on the responses produced by the LLM. These datasets are used to improve the LLM's performance on specific tasks, ensuring that the LLM is always performing at its best.


Finally, the LLM evaluates its own responses based on various metrics such as accuracy, relevance, and completeness. This evaluation acts as a feedback mechanism, guiding the LLM's learning process and helping it understand which aspects of its responses were effective and which need further improvement.


These advancements in LLM technology have significant implications. By enabling LLMs to self-update and continuously improve, we can ensure that these models are always up to date with the latest information and trends. This could be particularly useful in fields like medicine and finance, where staying up to date with the latest developments is crucial.


Furthermore, the ability of LLMs to generate smaller, highly focused models could make AI more accessible and useful on a personal level. For example, a person could have a personalized LLM on their phone that is highly knowledgeable about their specific health conditions or a financial situation that’s of interest. Or an LLM that is continuously updating itself with the latest information could be used to monitor for potential threats or to analyze complex security data.


Overall, the future of these smaller self-learning LLMs looks promising, with the potential to revolutionize many aspects of our lives and economy.


Sources:

[1] https://cloud.google.com/ai/llms

[2] https://arxiv.org/abs/2303.17651v2

[3] https://www.packtpub.com/article-hub/generating-synthetic-data-with-llms

[4] https://www.cureus.com/articles/149797-embracing-large-language-models-for-medical-applications-opportunities-and-challenges

[5] https://research.google/pubs/emergent-abilities-of-large-language-models/

[6] https://selfrefine.info

[7] https://arxiv.org/abs/2305.15041v1

[8] https://developers.google.com/machine-learning/resources/intro-llms

[9] https://openreview.net/forum?id=XD0PHQ5ry4

Sources

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