Kurylek, Bartosz;
Camara, Arthur;
Nandi, Akash;
Markopoulos, Evangelos;
(2024)
A Novel Agent-Based Framework for Conversational Data Analysis and Personal AI Systems.
Artificial Intelligence and Social Computing
, 122
pp. 124-136.
10.54941/ahfe1004649.
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Abstract
This paper introduces a novel agent-based framework that leverages conversational data to enhance Large Language Models (LLMs) with personalized knowledge, enabling the creation of Artificial Personal Intelligence (API) systems. The proposed framework addresses the challenge of collecting and analysing unstructured conversational data by utilizing LLM agents and embeddings to efficiently process, organize, and extract insights from conversations. The system architecture integrates knowledge data aggregation and agent-based conversational data extraction. The knowledge data aggregation method employs LLMs and embeddings to create a dynamic, multi-level hierarchy for organizing information based on conceptual similarity and topical relevance. The agent-based component utilizes an LLM Agent to handle user queries, extracting relevant information and generating specialized theme datasets for comprehensive analysis. The framework's effectiveness is demonstrated through empirical analysis of real-world conversational data and a user survey. However, limitations such as the need for further testing of scalability and performance under large-scale, real-world conditions and potential biases introduced by LLMs are acknowledged. Future research should focus on extensive real-world testing and the integration of additional conversational qualities to further enhance the framework's capabilities, ultimately enabling more personalized and context-aware AI assistance.
| Type: | Article |
|---|---|
| Title: | A Novel Agent-Based Framework for Conversational Data Analysis and Personal AI Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.54941/ahfe1004649 |
| Publisher version: | https://doi.org/10.54941/ahfe1004649 |
| Language: | English |
| Additional information: | © The Authors 2024. The authors of papers published in the AHFE Open Access Proceedings will retain full copyrights as specified by the provisions of the Creative Commons: (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Conversational AI, Agent-Based Systems, Large Language Models (LLMs) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > UCL School of Management |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10195658 |
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