Abstract: Implementations relate to a method implemented by one or more processors, the method including: receiving an input prompt for a large language model (LLM); generating first LLM output that is usable to generate a set of user interface (UI) each associated with a corresponding sub-prompt of the input prompt; causing, based on the first LLM output, the set of UI elements to be rendered at a user device; receiving further user input based on user interactions with one or more of the UI elements of the set of UI elements; and in response to determining that one or more termination conditions are satisfied: generating a final response to the input prompt based on generating second LLM output that is usable to generate the final response; and causing the final response to be rendered at the user device.

Abstract: Some implementations relate to generating a training and/or evaluation dataset with LLM prompts (e.g., derived from user queries) based on a prompt complexity. An input prompt, for example derived from a user query, is received. The input prompt is decomposed into a prompt tree comprising a plurality of nodes. The plurality of nodes comprise: a plurality of leaf nodes corresponding to simple sub-prompts of the input query; a plurality of branch nodes of sub-prompts each corresponding to multiple simple sub-prompts; and a root node corresponding to the input prompt. A prompt complexity is determined based on a path length of the prompt tree. The prompt complexity is compared to a threshold complexity. If the prompt complexity is above the threshold complexity, the input prompt is included in a set of training prompts and/or a set of evaluation prompts.

Abstract: Implementations relate to a method implemented by one or more processors, the method including: receiving natural language (NL) based input associated with a client device; generating, using a large language model (LLM) and based on processing the NL based input, LLM output; determining, based on the LLM output, a sequence of LLM responses, the sequence of LLM responses including at least one intermediate LLM response and a final LLM response. In some implementations, the method may further include causing the final LLM response to be rendered at the client device. In additional or alternative implementations, the method may further include storing, as an instance of training data for fine-tuning the LLM or an additional LLM, the NL based input along with the final LLM response.

Abstract: Implementations relate to improving instruction following capabilities of large language models (LLMs) using instruction decomposition, self-evaluation, and optionally progressive refinement. Processor(s) of a system can: obtain natural language (NL) based input, generate a plurality of candidate responses and evaluate the candidate responses based on instructions included in the NL based input, using an LLM, and progressively refine the candidate responses until it is determined that one or more termination criteria are satisfied. In some implementations, the NL based input can be received from a client device. In these implementations, a given candidate response that is progressively refined can be rendered for presentation at the client device and responsive to the NL base input. In additional or alternative implementations, the NL based input can be obtained from database(s). In these implementations, a given candidate response that is progressively refined can be utilized in fine-tuning of the LLM.

Abstract: Lubricating grease compositions have a titanium complex grease component along with a mineral/synthetic oil-based component and a conventional soap or grease. The conventional soaps and greases may be lithium complex, calcium sulfonate, or aluminum complex-based, among others, with and without additives. The compositions are high performance geases, exhibiting improved drop point, extreme pressure, antiwear, oil separation, and shelf life properties.