Adaptive LLM Selection for Accurate Document Data Extraction
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Solution Overview
Problem
Existing data extraction methods, such as rules-based approaches and neural networks, struggle with format changes and require significant retraining when document structures evolve, leading to inefficiencies and high computational costs.
Innovation Solution
Adaptive selection and deployment of large language models (LLMs) based on real-time accuracy benchmarks and user feedback, allowing for dynamic model switching and prompt refinement to optimize data extraction across varying document types and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based approaches are used for data extraction, then extraction accuracy is maintained for static document formats, but the system requires manual rule updates whenever document formats change
Solution Approach 1:
The system enables self-service by allowing the extraction model to automatically adapt to new document formats through few-shot learning. Instead of requiring manual rule updates, the system can learn new formats by observing a small number of examples, making the extraction process self-adjusting and reducing maintenance burden.
Solution Approach 2:
The system changes parameters by switching between different extraction models (rules-based, neural networks, LLMs) based on document characteristics. This parameter change allows the system to optimize for either accuracy with rules-based approaches or adaptability with LLMs, resolving the contradiction between these two requirements.
2Adaptability or versatility
If neural networks are trained to handle format changes, then adaptability to new document types improves, but retraining becomes computationally expensive and time-consuming
Solution Approach 1:
The system applies partial action by using few-shot learning with a small number of examples instead of full retraining. This allows the model to adapt to new formats with minimal computational resources and time, avoiding the need for extensive retraining while still achieving good adaptability.
Solution Approach 2:
The system prepares for format changes by maintaining a library of pre-trained models and using few-shot learning to quickly adapt to new formats. This preliminary preparation allows the system to respond rapidly to format changes without requiring time-consuming retraining campaigns.
3Measurement precision
If standard neural networks are used for extraction, then model accuracy can be achieved, but the system cannot handle significant format changes without retraining
Solution Approach 1:
The system achieves universality by implementing a multi-functional extraction pipeline that can handle both structured and unstructured documents, static and dynamic formats, using a combination of rules-based approaches, neural networks, and LLMs. This allows the system to maintain accuracy across diverse document types without requiring separate specialized models.
Solution Approach 2:
The system introduces dynamics by making the extraction approach adaptive rather than static. The system can dynamically switch between rules-based extraction, neural network extraction, and LLM-based extraction depending on the document characteristics, allowing it to maintain high accuracy while adapting to format changes.
4Reliability
If multiple extraction models are maintained for different document types, then extraction accuracy across diverse formats improves, but system complexity and maintenance burden increase
Solution Approach 1:
The system uses an intermediary document embedding model that converts diverse document formats into a unified representation. This intermediary layer allows different extraction models to work with a standardized input format, reducing the complexity of managing multiple specialized models while maintaining high extraction accuracy across diverse document types.
Data Source
AI summary
Techniques are described herein for adaptively selecting and deploying language models, such as large language models (LLMs), to extract data from electronic documents. User overrides of extracted data are tracked and used to compute accuracy benchmarks for multiple language models. The benchmark data may drive the selection of which language model is used to extract data in a given context. The process may select different language models for different contexts depending on which language model is most accurate for the given context. Attributes other than accuracy, such as cost and latency, may also be a factor in which language model is selected. The adaptive approach allows for ongoing improvement in data extraction through reinforcement feedback while optimizing for one or more target factors, such as model accuracy, latency, and/or cost.