LLM Answer Adaptation for Target Application Format Matching
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Solution Overview
Problem
Existing large language models (LLMs) often generate responses that require significant user input and modification to fit the context of a target application, such as emails or programming environments, due to their lack of awareness of the intended application format and style.
Innovation Solution
A system and method that adapts LLM-generated presentation content based on user input selecting a target application, using an assistant LLM to modify the content to match the target context, including audio or textual representations, and optionally refining the content based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM generates responses based on natural language queries, then the model can provide conversational experiences and information, but the generated responses require significant user input and modification to fit the target application context
Solution Approach 1:
The system performs preliminary action by detecting the target application context before the user completes their query. The contextual information from the target application is extracted and integrated into the prompt sent to the LLM, so the model receives pre-prepared context information that enables it to generate directly usable responses without requiring user modification.
Solution Approach 2:
The system introduces an intermediary component that acts as a bridge between the user's natural language query and the target application. This intermediary detects the target application context, processes it into relevant information, and combines it with the query to form an enhanced prompt for the LLM, thereby mediating the interaction and eliminating the need for manual user adaptation.
2Adaptability or versatility
If LLM generates general responses, then the model can maintain broad applicability, but the responses do not match the specific format and style requirements of target applications
Solution Approach 1:
The system applies local quality by extracting only the specific contextual information relevant to the target application from its full context. Rather than treating all context equally, it identifies and utilizes local properties of the target application (such as format requirements, style guidelines, and domain-specific conventions) to customize the LLM's response generation for that specific application.
Solution Approach 2:
The system changes parameters by dynamically adjusting the prompt based on the detected target application context. It modifies the input parameters sent to the LLM by incorporating context-specific information about the target application's format and style requirements, thereby changing the generation parameters to produce responses that precisely match the target application's needs.
3Extent of automation
If the system integrates target application context into LLM prompts, then the generated content can be directly input to target applications, but the system complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional architecture where a single context detection and integration mechanism serves multiple target applications. The same core functionality detects different target applications, extracts their specific contexts, and adapts the LLM prompts accordingly, allowing one system component to handle diverse applications without requiring separate customization for each.
Solution Approach 2:
The system implements self-service by enabling the context detection and integration process to operate automatically without requiring manual configuration or user intervention. The system autonomously detects the target application, extracts relevant context, formulates the enhanced prompt, and sends it to the LLM, making the entire adaptation process self-serveing and reducing operational complexity despite the sophisticated architecture.
Data Source
AI summary
A method includes receiving a natural language query directed toward an assistant large language model (LLM) specifying a particular action for the assistant LLM to perform. The method also includes generating, using the assistant LLM, presentation content based on performing the action specified by the natural language query and receiving a user input indication indicating selection of a target application after generating the presentation content. The method also includes adapting the presentation content generated by the assistant LLM based on the selected target application and providing the adapted presentation content for input to the selected target application.