Automated Knowledge Worker Using NLP Plan Generation
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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 maps requests to previous fulfilled requests, uses contextual data from a knowledge base, and prompts LLMs to create new plans, while also tracking and evaluating plan execution outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs are trained using vast amounts of text scraped from the Internet via self-supervised learning or semi-supervised learning, then the LLMs contain knowledge about syntax, semantics, and ontology data, but the training process requires extensive time and computational resources
Solution Approach 1:
The system performs preliminary action by pre-training LLMs on vast internet text corpora before deployment. This upfront training establishes the foundational knowledge base that enables the system to handle diverse knowledge worker tasks without requiring retraining for each specific task, thus resolving the contradiction between achieving comprehensive knowledge and the time required for training.
2Adaptability or versatility
If the automated general knowledge worker uses an NLP to generate a new plan of action for each request, then the system can handle ambiguous or insufficiently relevant requests, but the processing time and computational resources increase
Solution Approach 1:
The system segments the plan generation process into two distinct pathways: (1) a fast retrieval path that matches requests against a database of previously fulfilled requests using text searching with relevancy determination, and (2) a generation path that uses NLP/LLM to create new plans only when no suitable match is found. This segmentation resolves the contradiction by applying the computationally intensive NLP generation only when necessary, while using efficient retrieval for common requests.
Solution Approach 2:
The system copies successful plans from the database of previously fulfilled requests and adapts them to new requests when applicable. Instead of generating entirely new plans every time, the system retrieves and modifies existing proven plans, significantly reducing processing time while maintaining effectiveness for standard knowledge worker tasks.
3Productivity
If the system maps requests to previously fulfilled requests using text searching with relevancy determination, then the system can quickly retrieve existing plans, but the mapping may be ambiguous or insufficiently relevant for complex requests
Solution Approach 1:
The system implements feedback by evaluating the quality of matched plans and using execution outcomes to improve future matching. When a retrieved plan is executed and its outcome is evaluated, this feedback loop allows the system to learn from successes and failures, progressively improving the precision of request-to-plan mapping while maintaining fast retrieval speeds through the established database.
Data Source
AI summary
An automated general knowledge worker may be used to perform knowledge worker tasks. The automated general knowledge worker may receive a text-based request to perform a knowledge worker task for an organization from a computing device. The automated general knowledge worker may search a knowledge base to obtain contextual data of the organization that are relevant to the text-based request. The automated general knowledge worker may provide text information included in the text-based request and the contextual data obtained from the knowledge base in text form to a natural language processor (NLP) to prompt the NLP to generate a new plan of action for performing the knowledge worker task. Subsequently, the automated general knowledge worker may execute the new plan of action to perform the knowledge worker task for the organization.