Video Transcript Context Selection for Accurate LLM Browser Answers
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Solution Overview
Problem
Conventional machine learning models lack proper background and context for generating answers, often providing irrelevant, too simple or complex outputs, and may generate fake data, while being inefficient in data transmission.
Innovation Solution
A system that preprocesses prompts and selects data domains from a text transcript to train a machine learning model, using a confidence metric to ensure relevance and accuracy of answers, and incrementally utilizes additional domains if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning models are used to generate answers, then the system can provide quick responses, but the answers may be irrelevant, too simple or complex, or contain fake data due to lack of proper background and context
Solution Approach 1:
The system performs preliminary actions by selecting and providing relevant context data from the video transcript to the machine learning model before generating answers. This ensures the model has the necessary background information upfront, improving answer accuracy while maintaining response speed.
Solution Approach 2:
Instead of providing all possible context data, the system selectively provides only the relevant local portions of the video transcript that pertain to the specific question being asked. This targeted approach ensures accuracy without overwhelming the model with unnecessary information.
2Reliability
If all data from the video transcript is provided to the machine learning model, then the model has comprehensive context, but data transmission becomes inefficient and resource-intensive
Solution Approach 1:
The system extracts only the necessary portions of data from the complete video transcript based on the question's context and timestamp. This extraction process removes redundant information, providing sufficient context for accurate answers while significantly reducing data transmission requirements.
Solution Approach 2:
The video transcript is segmented into relevant portions based on the question's temporal and contextual requirements. Instead of transmitting the entire transcript, only the pertinent segments are provided to the model, improving transmission efficiency while maintaining data completeness for the specific query.
3Reliability
If the machine learning model is trained with multiple data domains, then the answer accuracy improves, but the system complexity and training time increase
Solution Approach 1:
The system dynamically adapts the amount and type of context data provided to the model based on the specific question being asked. Rather than using a fixed complex multi-domain training approach, the system flexibly selects relevant data portions, reducing system complexity while maintaining high answer accuracy through adaptive data selection.
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for prompting a machine learning model to generate answer data based on a recording. Some embodiments involve preprocessing a prompt corresponding to a query for a first system by receiving the prompt and a timestamp corresponding to a time position of the query in a recording, acquiring a text transcript based on the recording, and selecting, based on the timestamp and the text transcript, a first data domain from the text transcript. Some embodiments involve transmitting at least one of the prompt, the text transcript, and the first data domain to a second system, the second system including a machine learning model. Some embodiments involve generating answer data corresponding to the prompt by querying the machine learning model with the prompt, receiving answer data from the machine learning model, and transmitting the answer data to the first system.