Two-Stage LLM Question Generation for Inference Output Confidence
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Solution Overview
Problem
Evaluating input data for inference models to determine confidence levels is time-consuming and prone to human error, affecting the reliability and efficiency of computer-implemented services.
Innovation Solution
Utilizing a first large language model (LLM) to identify leading indicators and emerging trends from inference model input data, and a second LLM to generate questions that guide downstream consumers in establishing confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual evaluation of input data is performed to establish confidence levels, then reliability of decision-making is improved, but time consumption and resource usage increase
Solution Approach 1:
The system enables automatic self-evaluation of input data quality by the inference model itself through generated questions and answers, eliminating the need for manual expert evaluation while maintaining reliability in confidence level determination
Solution Approach 2:
Manual mechanical evaluation processes are replaced with an automated system using large language models to generate and answer questions about input data, substituting human effort with computational processes that achieve the same reliability goal
2Reliability
If comprehensive input data evaluation is performed to establish confidence levels, then decision reliability is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary automated evaluation of input data quality before the inference model generates outputs, establishing confidence levels in advance so that downstream consumers can quickly determine whether to use the outputs without delay
Solution Approach 2:
Comprehensive manual evaluation processes are replaced with automated large language model-based question generation and answering, maintaining thorough assessment of input data while dramatically reducing time consumption and preserving productivity
3Measurement precision
If manual data analysis is performed to identify leading indicators and trends, then interpretation accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The inference model automatically generates questions about its own input data and answers them using large language models, enabling self-interpretation of leading indicators and trends without requiring manual analysis while maintaining high accuracy
Solution Approach 2:
Large language models serve as intermediaries between raw input data and human users, automatically generating and answering questions that reveal leading indicators and emerging trends, thereby providing accurate interpretation without requiring users to perform complex manual analysis
Data Source
AI summary
Methods and systems for interpreting outputs from inference models are disclosed. To establish a level of confidence in the outputs, ingest data utilized by the inference models to generate the outputs may be investigated. To efficiently investigate ingest data to identify portions of the ingest data that had a highest contribution to generation of the outputs, a first large language model (LLM) may ingest the output, the ingest data, and a set of queries to be answered by the first LLM. The first LLM may generate leading indicators and emerging trends for a portion of the outputs. The leading indicators, the emerging trends, and a set of question generation templates may be fed into a second LLM to generate one or more questions. The one or more questions may be usable to establish a level of confidence in the outputs.