Multi-LLM Output Comparison With Preference-Based Model Prioritization
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Solution Overview
Problem
Current technologies lack a way to adequately gauge the quality of various large language models (LLMs) and select the best one for a given implementation, leading to inefficiencies in LLM utilization.
Innovation Solution
A system that receives inputs from multiple LLMs, presents their generative outputs concurrently, allows user selection and editing, and uses a learning component to prioritize LLMs based on user preferences, adjusting future outputs accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple LLMs are used to generate outputs, then the quality and variety of generative text is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The system segments the LLM evaluation process by dividing generative tasks across multiple specialized LLMs, each handling specific aspects of text generation. This allows comparison of specialized outputs while maintaining manageable system architecture through modular organization of model instances.
Solution Approach 2:
The user interface acts as an intermediary layer that manages the complexity of multiple LLMs by providing unified access points, comparison views, and selection mechanisms. This mediator abstracts the underlying system complexity from users while enabling quality assessment across multiple model outputs.
2Ease of operation
If user preferences are tracked and used to prioritize LLM outputs, then the ease of operation is improved, but the extent of automation and data processing required increases
Solution Approach 1:
The system implements feedback loops where user selections and interactions with generated text are tracked and fed back into the model selection process. This automated preference tracking learns from user behavior to prioritize favored LLMs, reducing manual intervention while improving operational ease through adaptive model routing.
Solution Approach 2:
The system provides self-service functionality by automatically adapting to user preferences without requiring manual configuration. The automated learning component serves itself by continuously improving model selection based on observed user patterns, reducing the burden on users while maintaining high automation levels.
Data Source
AI summary
In one aspect, a device includes a processor system and storage. The storage includes instructions executable by the processor system to receive a prompt, and to provide the prompt as first input to a first large language model (LLM). Based on providing the prompt as first input to the first LLM, the instructions are executable to receive a first output from the first LLM that indicates first generative text. The instructions are further executable to provide the prompt as second input to a second LLM, and to receive a second output from the second LLM based on providing the prompt as second input to the second LLM. The second output also indicates second generative text. The instructions are then executable to concurrently present, on a display, the first and second generative texts. Present principles may also be applied to generative image and generative audio models.