The performance of an agent within the current large language model framework is influenced not only by the model itself but also by several other factors. Even if the large language model is not the limiting factor, optimizing these other aspects can enhance the overall effectiveness of the agent. Take Kimi as an example; its ability to stand out might indeed be due to targeted improvements in certain aspects of the large model. However, the key lies in its enhanced capability to parse document types, which has effectively improved the user experience in practical applications. Kimi can process long documents by dividing them into sections and using iterative retrieval to generate answers, significantly enhancing the application experience of the agent in specific scenarios.
I'd love to get this in a png or gif to print out at high resolution. Do you have that?
The performance of an agent within the current large language model framework is influenced not only by the model itself but also by several other factors. Even if the large language model is not the limiting factor, optimizing these other aspects can enhance the overall effectiveness of the agent. Take Kimi as an example; its ability to stand out might indeed be due to targeted improvements in certain aspects of the large model. However, the key lies in its enhanced capability to parse document types, which has effectively improved the user experience in practical applications. Kimi can process long documents by dividing them into sections and using iterative retrieval to generate answers, significantly enhancing the application experience of the agent in specific scenarios.
mi occupo da quasi 2 anni di preparare e addestrare Assistenti A.I. personalizzati per professionisti e aziende e il tuo articolo è molto interessante
Thank you!