Capability-Tuned LLM Control for ERP Service Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing large language models (LLMs) face limitations in handling capability information due to input token length issues and computational inefficiencies, leading to inaccurate results when matching user needs with service capabilities in ERP systems.
Innovation Solution
Fine-tuning LLMs using Parameter-Efficient Fine Tuning (PEFT) and Low-Rank Adaptation (LoRA) techniques to create a fine-tuned LLM that understands capability definitions, leveraging bidirectional encoders and generative adversarial networks to improve contextual understanding and reduce computation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs are used to match user needs with service capabilities, then contextual understanding is improved, but input token length limitations and computational inefficiency occur
Solution Approach 1:
The patent segments the capability definitions into structured formats with specific sections (intent, description, API) and processes them through bidirectional encoders separately before generating matching results. This segmentation reduces the token processing burden while maintaining contextual understanding.
Solution Approach 2:
The patent changes the parameter of model architecture by introducing bidirectional encoders and generative adversarial networks, transforming the unidirectional processing into bidirectional contextual analysis. This parameter change improves understanding accuracy without linearly increasing computation time.
2Measurement precision
If LLMs process capability definitions, then matching accuracy is improved, but input token length becomes unwieldy
Solution Approach 1:
The patent divides capability definitions into structured sections (intent, description, API) and processes them through bidirectional encoders that capture contextual relationships without requiring the entire text to be processed as a single token sequence, effectively managing token length.
Solution Approach 2:
The patent introduces bidirectional encoding that processes text in both forward and backward directions, adding a dimensional aspect to token processing. This allows the model to understand contextual relationships without proportionally increasing the linear token length burden.
3Productivity
If traditional search methods are used for capability matching, then computation time is reduced, but accuracy in understanding human language nuances is insufficient
Solution Approach 1:
The patent performs preliminary action by using bidirectional encoders to pre-process and encode capability definitions into structured representations with captured contextual relationships. This preliminary encoding enables faster subsequent matching while maintaining high language understanding accuracy.
Solution Approach 2:
The patent introduces bidirectional encoders and generative adversarial networks as intermediary components between traditional search methods and LLM processing. These intermediaries pre-process and structure the data, enabling both speed and accuracy in capability matching.
4Adaptability or versatility
If LLM input tokens are increased to handle more capabilities, then matching comprehensiveness is improved, but computation time grows exponentially
Solution Approach 1:
The patent segments capability processing into modular bidirectional encoder units that can handle multiple capabilities independently. This segmentation allows comprehensive matching across many capabilities without exponentially increasing computation time, as each segment processes information efficiently and independently.
Data Source
AI summary
In an example embodiment, capability definitions are leveraged to fine-tune an LLM, producing a fine-tuned LLM that is specifically designed to handle capability definitions, without the need to provide such capability definitions as input tokens to an LLM. This fine-tuned LLM has a deeper understanding of the capabilities of a system than generic LLMs.