A paradigm shift in human-machine collaboration driven by ChatGPT and Large Language Models (LLMs)
The increasing trend of using large language models, including GPT-3/ChatGPT, has been widely discussed lately. The influence of this technology extends beyond the realm of Artificial Intelligence and is predicted to revolutionize the software industry as a whole.
Why is LLMs technology disruptive?
LLMs provide a general capability of natural language processing (NLP), which can accomplish different NLP tasks based on a pretrained model. This disrupts the research and development of NLP. In traditional NLP, classification, extraction, question-answering, summarization, translation, and other tasks are treated as separate tasks requiring different models and methods. In contrast, LLM represented by GPT-3/ChatGPT treats various NLP tasks as text generation tasks and has demonstrated its excellent performance in performing multiple NLP tasks. In the future, priority should be given to LLMs over any other methods for addressing NLP problems.
The generality and openness of LLMs allow building an AI ecosystem around them. Two types of roles exist in this ecosystem - LLM providers and application developers based on LLMs. LLM providers, such as OpenAI, develop general LLMs and offer developers with model inference and fine-tuning capabilities through APIs. Application developers utilize the capabilities of LLMs in their software or systems to create value for their users or customers. This will create a sustainable and healthy ecosystem where LLM providers focus on developing high-quality general models, while application developers utilize LLMs to address specific business problems, accumulate domain data, and create their unique competitive advantages.
The rapid growth of large language model applications will bring about a significant paradigm shift in user interface and human-machine collaboration. LLMs use natural language for input and output, allowing humans and machines to interact using natural language, which reduces the cost for people to learn and use software. Moreover, ChatGPT demonstrates the possibility of teaching robots through conversation, enabling a new form of human-machine collaboration. It is expected to see that most software in the future will have natural language based user interfaces and enable human-machine collaboration at scale.
How are LLMs related to the Work Execution System?
The capabilities demonstrated by LLMs are critical to closing the work execution gap at both the organizational and employee levels.
At the organizational level, the work execution gap mainly refers to complex processes and a massive amount of un-structured data, which makes executives lose control and insights over the organization. Because LLMs naturally use un-structured data for self-supervised training, organizations can train a model containing enterprise and business "knowledge" on the basis of general LLMs to help executives regain insights and control over the organization.
At the employee level, the work execution gap mainly refers to the fact that employees are not given intuitive IT tools and solutions to automate low-value, repetitive tasks to better control their work. Products built with LLMs such as ChatGPT are straightforward to use and can complete various tasks, and are expected to change this situation in a fundamental way.
Potential applications of LLMs in Laiye's products?
As mentioned earlier, LLMs provide general NLP capabilities. Hence, most NLP tasks in Conversational AI/Chatbot, RPA, and IDP products are likely to be solved by LLMs. The following list outlines several potential applications of LLMs in Laiye's products.
Please note that the above list does not fully capture the extensive range of applications of Large Language Models (LLMs) in our products. We are committed to fully embracing the potential of LLMs by investing significantly in research and development to incorporate them into both our present and future product offerings.
Contact us to learn more about how it can work for your organization.
