Language Model Dynamic Output Generation via Retrieval Rules
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models are limited in their ability to provide responses based on new information, as they are trained using a fixed set of training data and may not accurately respond to queries involving information generated after the model was trained.
Innovation Solution
A method and system that generate dynamic tokens by using retrieval rules within the language model to search for and incorporate up-to-date information into the response, allowing the model to provide answers that include dynamic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model is trained using a fixed set of training data, then the model can provide consistent responses based on known information, but the model cannot accurately respond to queries involving new information generated after training
Solution Approach 1:
The patent implements a dynamic response generation system where the language model can switch between using its trained knowledge and retrieving external information based on the query. The system generates a response indicator that dynamically determines whether to use internal model knowledge or external search results, allowing the model to adapt to both historical and current information needs while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary mechanism (the response indicator and retrieval module) between the language model and the final response output. This intermediary component decides whether to use the language model's internal knowledge or to retrieve information from external sources, thereby resolving the contradiction between relying on trained data and accessing new information.
2Productivity
If the language model relies solely on its trained knowledge, then the response generation process is simple and fast, but the model lacks access to up-to-date information
Solution Approach 1:
The patent applies partial action by using the language model's internal knowledge for queries that can be answered from training data, while only invoking external retrieval when necessary. The response indicator mechanism determines when to use the simpler, faster internal knowledge path versus when to perform the more complex external retrieval, thereby maintaining high productivity while preventing information loss for current events.
Data Source
AI summary
Methods and systems for answering a query include generating first tokens in response to an input query using a language model, the first tokens including a retrieval rule. A retrieval rule is used to search for information to generate dynamic tokens. The retrieval rule in the first tokens is replaced with the dynamic tokens to generate a dynamic partial response. Second tokens are generated in response to the input query. The second tokens are appended to the dynamic partial response to generate an output responsive to the input query.