Language Model Recommendation Using Cross-Model Response Evaluation
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Solution Overview
Problem
Existing generative AI systems using large language models (LLMs) lack the ability to appropriately select and utilize models that can effectively respond to user requests, leading to suboptimal response quality.
Innovation Solution
A recommendation system that includes a task executing section, evaluation information acquiring section, and recommendation information generating section, which selects a recommended language model based on user input, evaluation information from a database, and generates a message to present the recommended model to the user, enhancing response quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user selects a language model from a plurality of models, then the user can specify their preferred model, but the system cannot ensure that the selected model is the most appropriate for the specific request
Solution Approach 1:
The patent introduces a recommendation model as an intermediary between the user's model selection and the actual request processing. This recommendation model evaluates the user's request characteristics and the available language models to suggest the most appropriate model, thereby resolving the conflict between user autonomy and system optimality without completely overriding user choice
Solution Approach 2:
The system implements a feedback mechanism where the recommendation model continuously learns from evaluation information about model performance on different types of requests. This feedback loop enables the system to improve its model selection accuracy over time while maintaining user control over the final model choice
2Reliability
If the system evaluates multiple language models to find the best match, then response quality improves, but system complexity increases
Solution Approach 1:
The patent segments the model selection process into distinct components: a recommendation model that evaluates requests, an evaluation information management component that stores model performance data, and a model selection component that synthesizes this information. This segmentation allows each component to specialize in one aspect of the selection process, improving overall system effectiveness while making the complexity more manageable
Solution Approach 2:
The system performs preliminary evaluation of language models by pre-computing and storing evaluation information about each model's performance characteristics before actual request processing. This preliminary action enables faster, more informed model selection during runtime without adding significant complexity to the request processing flow
3Measurement precision
If the system stores evaluation information for multiple language models, then model selection accuracy improves, but information storage requirements increase
Solution Approach 1:
The patent extracts only the most relevant evaluation information from comprehensive model performance data, focusing on key metrics that directly impact model selection for different request types. By taking out only the essential evaluation criteria needed for effective model matching, the system maintains high selection accuracy while minimizing storage requirements
Data Source
AI summary
In a recommendation system, a task executing section accepts a request including text data from a user, and causes a first language model which is one language model specified by the user from a plurality of language models to generate a first response to the request. An evaluation information acquiring section acquires, from a predetermined database, evaluation information about each of the plurality of language models. A recommendation information generating section causes a second language model whose execution environment is different from the first language model to select a recommended language model for generating a response to the request from the plurality of language models on the basis of the request, the first response, and the evaluation information. Furthermore, the recommendation information generating section causes the second language model to generate recommendation information including a message to present the recommended language model to the user.