Search-Grounded Text Processing for Timely Language Model Replies
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Solution Overview
Problem
Large language models lack the ability to incorporate and learn new natural languages in real time, leading to information disconnection and poor timeliness in text generation, resulting in limited content scope and low controllability.
Innovation Solution
A method involving a search engine interface to obtain text search results, select relevant answer text segments, and invoke a language model to generate timely replies based on the query text and reference text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a large language model is used to generate text, then the text generation capability is improved, but the timeliness and ability to incorporate new information deteriorates
Solution Approach 1:
The system segments the text generation process into two independent parts: (1) a search engine component that retrieves up-to-date information, and (2) a language model component that generates text based on retrieved information. This segmentation allows each component to operate independently, maintaining timeliness through frequent search engine updates while preserving the language model's generation capabilities.
Solution Approach 2:
The system introduces an intermediary mechanism (search engine interface) between the language model and the information source. This intermediary enables the language model to access current information without requiring direct updates to the model itself, thus maintaining timeliness while preserving the model's stable generation capabilities.
2Manufacturing precision
If a large language model with huge parameters is used, then the text generation quality is improved, but the deployment difficulty increases
Solution Approach 1:
The system merges the language model with an external search engine interface to create a hybrid system. This combination allows the use of high-quality language models for text generation while offloading the complexity of real-time information retrieval and updates to the search engine, thereby reducing overall deployment difficulty.
Solution Approach 2:
The system makes the language model multi-functional by combining it with search capabilities. Instead of requiring the language model to handle both generation and information retrieval, the system creates a universal text generation system that leverages the strengths of both components, simplifying deployment requirements.
3Device complexity
If the language model operates without external information sources, then the system complexity is reduced, but the relevance and accuracy of generated text deteriorates
Solution Approach 1:
The system performs preliminary information retrieval through the search engine before the language model generates text. This preliminary action ensures that the language model receives up-to-date and relevant information, improving text accuracy and relevance without significantly increasing system complexity.
Data Source
AI summary
A text processing method includes obtaining a query text, invoking a search engine interface based on the query text to obtain a plurality of text search results corresponding to the query text, obtaining, from the plurality of text search results, a plurality of answer text segments matching the query text, determining a relevance between the query text and each of the plurality of answer text segments, determining one of the plurality of answer text segments that corresponds to a maximum relevance as a reference text of the query text, and invoking a language model based on the query text and the reference text to obtain a reply text of the query text.