Enabling device control planning capabilities of small language model

A method for enabling an improved device control capability of a first language model transferrable to a hub device configured to be operable by a user in an environment, the method comprising:

• generating, by using a second language model, a pool of diverse instructions including direct instructions for controlling a first group of electronic devices and indirect instructions for controlling the first group of the electronic devices, wherein the first group of the electronic devices corresponds to all available devices in the environment;
• generating, by using the second language model, first plans related to operations of controlling the first group of the electronic devices;
• determining, by using the second language model and a retrieval model, retrieved devices based on the first plans and the indirect instructions, wherein the retrieved devices correspond to a second group of the electronic devices and wherein a first number of the electronic devices in the first group is higher than a second number of the electronic devices in the second group;
• generating, by using the second language model, second plans based on a third group of the electronic devices, wherein a third number of the electronic devices in the third group corresponds to a number of the first number minus the second number;
• generating a data set by combining the first plans and the second plans;
• performing a fine-tuning the first language model based on the data set;
• generating computer codes corresponding to the fine-tuned first language model;
• and
• transferring the generated computer codes to the hub device to be connected with a fourth group of the electronic devices in the environment.

An electronic device for enabling an improved device control capability of a small language model (SLM), the electronic device comprising:

• at least one processor comprising processing circuitry;
• and
• at least one memory including one or more instructions, which executed by the at least one processor individually or collectively, to cause the electronic device to at least:
• generate, by using a large language model (LLM), a pool of diverse instructions including direct instructions for controlling a first group of electronic devices and indirect instructions for controlling the first group of the electronic devices, wherein the first group of the electronic devices corresponds to all available devices in the environment;
• generate, by using the LLM, base plans related to operations of controlling the first group of the electronic devices;
• determine, by using the LLM and a retrieval model, retrieved devices based on the base plans and the indirect instructions, wherein the retrieved devices correspond to a second group of the electronic devices and wherein a first number of the electronic devices in the first group is higher than a second number of the electronic devices in the second group;
• generate, by using the LLM, contrastive plans based on a third group of the electronic devices, wherein a third number of the electronic devices in the third group corresponds to a number of the first number minus the second number;
• generate a data set by combining the base plans and the contrastive plans;
• perform a fine-tuning the SLM based on the data set;
• generate computer codes corresponding to the fine-tuned SLM;
• and
• transfer the generated computer codes to the hub device to be connected with a fourth group of the electronic devices in the environment.