eprintid: 10195658
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/19/56/58
datestamp: 2024-08-09 13:03:29
lastmod: 2024-08-09 13:03:29
status_changed: 2024-08-09 13:03:29
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Kurylek, Bartosz
creators_name: Camara, Arthur
creators_name: Nandi, Akash
creators_name: Markopoulos, Evangelos
title: A Novel Agent-Based Framework for Conversational Data Analysis and Personal AI Systems
ispublished: pub
divisions: UCL
divisions: B04
divisions: F49
keywords: Conversational AI, Agent-Based Systems, Large Language Models (LLMs)
note: © 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/).
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.
date: 2024
date_type: published
official_url: https://doi.org/10.54941/ahfe1004649
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2303991
doi: 10.54941/ahfe1004649
lyricists_name: Markopoulos, Evangelos
lyricists_id: EMARK80
actors_name: Markopoulos, Evangelos
actors_name: Kalinowski, Damian
actors_id: EMARK80
actors_id: DKALI47
actors_role: owner
actors_role: impersonator
full_text_status: public
publication: Artificial Intelligence and Social Computing
volume: 122
pagerange: 124-136
citation:        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 <https://doi.org/10.54941/ahfe1004649>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10195658/1/Markopoulos_A%20Novel%20Agent-Based%20Framework%20for%20Conversational%20Data%20Analysis%20and%20Personal%20AI%20Systems_VoR.pdf