Semantic Caching for Location-Aware Question Answering
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Solution Overview
Problem
Current inter-document search systems, such as open-domain question answering, face computational prohibitions and limitations in providing accurate results due to the need for large language models and assumptions of single correct answers, which fail to account for location-dependent answers.
Innovation Solution
A method involving a processor that generates query embeddings, compares them to question embeddings, and uses a transformer model to identify and cache question-answer mappings, allowing for location-aware answers and efficient query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to achieve high accuracy in open-domain question answering, then answer accuracy is improved, but computational resource requirements and processing time increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-computing question embeddings and storing them in a database before actual queries arrive. When a query is received, the system compares it against pre-computed embeddings rather than performing full document analysis, significantly reducing real-time computational requirements while maintaining answer accuracy
Solution Approach 2:
The system creates simplified copies of questions in embedding form and stores them in a database. These question embeddings serve as lightweight representations that can be quickly compared against query embeddings without requiring access to the full original documents or large language models during query processing
2Productivity
If precomputed question to answer extraction is used to avoid excessive compute times, then processing speed is improved, but the number of answerable queries is significantly limited
Solution Approach 1:
The system uses universal question embeddings that can represent multiple variations of the same question. A single pre-computed question embedding can match multiple different query formulations, allowing the system to handle diverse query types with a limited set of pre-computed answers, thus improving both processing speed and adaptability
Solution Approach 2:
The system changes the parameter representation from text-based questions to embedding-based questions. This transformation allows for flexible matching where small variations in query wording result in similar embedding representations, enabling the system to answer a broader range of queries using pre-computed embeddings without sacrificing processing speed
3Device complexity
If current systems assume a single correct answer per question, then processing complexity is reduced, but accuracy fails when multiple location-dependent answers exist
Solution Approach 1:
The system dynamically determines the number of answers to return based on the query and context. Rather than assuming a fixed single answer, the system can retrieve multiple candidate answers from the database and rank them by relevance, allowing it to handle both single-answer and multi-answer scenarios with the same infrastructure
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with answers (such as selections, corrections, or rejections) are used to refine future answer retrieval. This feedback loop improves accuracy over time by learning which answers are most relevant for different query types and location contexts without increasing processing complexity
Data Source
AI summary
The disclosure relates to an offline-online question answering system. In some aspects, the techniques described herein relate to a method including: receiving, by a processor, a query from a user; generating, by the processor, a query embedding representing the query; identifying, by the processor, at least one question corresponding to the query by comparing the query embedding to a plurality of embeddings of questions; and transmitting, by the processor, an answer corresponding to the at least one question to the user in response to the query.