Noviello, Yuri
(2024)
Enhancing Chatbot Efficacy in Italian Language through Retrieval Augmented Generation and LoRA Fine-Tuning.
[Laurea magistrale], Università di Bologna, Corso di Studio in
Artificial intelligence [LM-DM270], Documento ad accesso riservato.
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Abstract
This thesis delves into the integration of Large Languages Models (LLM)within the implementation of conversational agents. Traditionally, chatbotsystems were built using rule-based or simple machine learning approachesthat heavily relied on predefined scripts and limited user input interpretation.
Addressing these challenges, our research proposes a methodology thatleverages the advanced capabilities of LLMs, enhanced through Retrieval-Augmented Generation (RAG) systems and the Low-Rank Adaptation (LoRA)fine-tuning technique, to significantly improve chatbot performance on theItalian language.
Through meticulous evaluation, including the most recent benchmarks de-signed for assessing LLMs, our research demonstrated the superiority of thefine-tuned model over the original one in handling Italian conversations byleveraging only a minimal subset of model’s parameters.
This research was conducted internally the ML/AI team of Injenia, withinthe European project Masters in AI for Careers in EU (MAI4CAREU).
Abstract
This thesis delves into the integration of Large Languages Models (LLM)within the implementation of conversational agents. Traditionally, chatbotsystems were built using rule-based or simple machine learning approachesthat heavily relied on predefined scripts and limited user input interpretation.
Addressing these challenges, our research proposes a methodology thatleverages the advanced capabilities of LLMs, enhanced through Retrieval-Augmented Generation (RAG) systems and the Low-Rank Adaptation (LoRA)fine-tuning technique, to significantly improve chatbot performance on theItalian language.
Through meticulous evaluation, including the most recent benchmarks de-signed for assessing LLMs, our research demonstrated the superiority of thefine-tuned model over the original one in handling Italian conversations byleveraging only a minimal subset of model’s parameters.
This research was conducted internally the ML/AI team of Injenia, withinthe European project Masters in AI for Careers in EU (MAI4CAREU).
Tipologia del documento
Tesi di laurea
(Laurea magistrale)
Autore della tesi
Noviello, Yuri
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
AI,NLP,RAG,LLM,LLAMA
Data di discussione della Tesi
19 Marzo 2024
URI
Altri metadati
Tipologia del documento
Tesi di laurea
(NON SPECIFICATO)
Autore della tesi
Noviello, Yuri
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Ordinamento Cds
DM270
Parole chiave
AI,NLP,RAG,LLM,LLAMA
Data di discussione della Tesi
19 Marzo 2024
URI
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