Automated Knowledge Worker Using NLP Mapping
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Solution Overview
Problem
Current natural language processing technologies, particularly Large Language Models (LLMs), face challenges in efficiently and accurately generating plans of action for knowledge worker tasks within organizational contexts, often requiring extensive training data and struggling with ambiguous or insufficiently relevant requests.
Innovation Solution
An automated general knowledge worker system that enables human users to interact using natural language, leveraging a natural language processor (NLP) to generate plans of action. This system searches a knowledge base for relevant contextual data, maps requests to previous fulfilled requests, and uses LLMs to create new plans based on user inputs and organizational data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Large Language Models are trained using vast amounts of text scraped from the Internet, then the model's knowledge base is enriched, but the training time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training the LLM on vast internet text corpora to establish general knowledge and language patterns. During deployment, it then leverages this pre-trained foundation to quickly generate responses for specific organizational tasks without requiring retraining on each new task, thus resolving the contradiction between data volume and training time.
Solution Approach 2:
The training process is segmented into two phases: (1) pre-training on general internet text to build foundational knowledge, and (2) fine-tuning on organizational-specific data for task optimization. This segmentation allows the system to accumulate vast training data efficiently by reusing the pre-trained model rather than retraining from scratch for each organizational context.
2Measurement precision
If the system attempts to map requests to previously fulfilled requests using search techniques, then relevant plans can be retrieved, but ambiguous or insufficiently relevant requests cannot be adequately handled
Solution Approach 1:
The system implements feedback mechanisms where the LLM analyzes the relevance and ambiguity of search results, and iteratively refines its understanding by requesting clarification from users when mappings are ambiguous. This feedback loop enables the system to maintain high mapping accuracy for clear requests while adapting to handle ambiguous requests through interactive clarification, resolving the contradiction between precision and versatility.
Solution Approach 2:
The system changes the parameter of request interpretation by using the LLM's contextual understanding and semantic analysis capabilities to adapt the mapping process. Instead of rigid keyword matching, the system dynamically adjusts its interpretation based on the request's ambiguity level, allowing it to maintain precision for clear requests while becoming versatile enough to handle ambiguous ones through contextual inference and user clarification.
3Extent of automation
If the system uses NLP to generate plans of action based on user inputs and organizational data, then task automation is achieved, but the complexity of the system increases
Solution Approach 1:
The system achieves self-service automation by using the LLM to autonomously generate plans of action, execute tasks, and learn from outcomes without requiring complex external control systems. The model processes user inputs, searches knowledge bases, formulates action plans, and executes tasks independently, reducing the need for sophisticated system architecture while maintaining high automation levels.
Solution Approach 2:
The LLM serves multiple functions within the system: it processes user inputs, searches for relevant information, generates action plans, executes tasks, and learns from outcomes. This multi-functionality consolidates what would otherwise require multiple separate systems and components into a single versatile model, achieving high automation while minimizing system complexity.
4Reliability
If the system executes plans and tracks outcomes to use as examples for future plans, then continuous improvement is achieved, but the data processing and storage requirements increase
Solution Approach 1:
The system extracts only the essential learning signals from executed plans and their outcomes, storing summarized representations rather than raw data. By taking out and storing only the critical information needed for future plan generation (such as success/failure patterns and key learnings), the system achieves continuous improvement while minimizing data storage requirements.
Solution Approach 2:
The system discards redundant or less valuable execution details while recovering and storing only the essential learning patterns from outcomes. This selective retention approach allows the system to learn from executed plans for continuous improvement without accumulating excessive data, thus maintaining high reliability while controlling storage requirements.
Data Source
AI summary
An automated general knowledge worker may use information stored in a knowledge base to provide a plan of action for performing a knowledge worker task. A plan of action for performing a knowledge worker task for an organization may be stored in a knowledge base. A text-based prompt for mapping the plan of action to a version of a classification hierarchy for the organization may be sent to a natural language processor (NLP). In return, a mapping of the plan of action to one or more nodes in the version of the classification hierarchy may be received from the NLP. Accordingly, the mapping of the plan of action to the one or more nodes in the version of the classification hierarchy may be stored in the knowledge base.