17549201. AUTOMATED FEW-SHOT LEARNING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE-BASED QUERY ANSWERING SYSTEMS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
AUTOMATED FEW-SHOT LEARNING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE-BASED QUERY ANSWERING SYSTEMS
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Saneem Ahmed Chemmengath of Bangalore (IN)
Vishwajeet Kumar of Bangalore (IN)
Samarth Bharadwaj of Bangalore (IN)
AUTOMATED FEW-SHOT LEARNING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE-BASED QUERY ANSWERING SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17549201 titled 'AUTOMATED FEW-SHOT LEARNING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE-BASED QUERY ANSWERING SYSTEMS
Simplified Explanation
The patent application describes automated few-shot learning techniques for artificial intelligence-based query answering systems. Here are the key points:
- The method involves obtaining sets of queries and answers associated with tables.
- The complexity of the queries is determined, and new queries are generated based on this complexity.
- The new queries are annotated and used to train artificial intelligence-based query answering systems.
- The trained systems can then perform automated actions based on the learned information.
Potential applications of this technology:
- Improving the accuracy and efficiency of query answering systems.
- Enhancing the performance of AI-based virtual assistants and chatbots.
- Streamlining data analysis and decision-making processes.
Problems solved by this technology:
- Limited availability of annotated data for training AI systems.
- Difficulty in handling complex queries and providing accurate responses.
- Time-consuming manual annotation and training processes.
Benefits of this technology:
- Enables efficient training of AI systems with limited annotated data.
- Enhances the ability of AI systems to handle complex queries.
- Automates query answering processes, saving time and effort.
Original Abstract Submitted
Methods, systems, and computer program products for automated few-shot learning techniques for artificial intelligence-based query answering systems are provided herein. A computer-implemented method includes obtaining multiple sets of queries and answers associated with one or more tables; determining a level of complexity attributed to at least a portion of the queries from the multiple sets of queries and answers; determining, based at least in part on the determined level of complexity attributed to the at least a portion of the queries, one or more new queries for use in training at least one artificial intelligence-based query answering system; facilitating annotation of the one or more new queries; training the at least one artificial intelligence-based query answering system using at least a portion of the one or more annotated new queries; and performing at least one automated action using the at least one trained artificial intelligence-based query answering system.
