Unipa-GPT: a framework to assess open-source alternatives to Chat-GPT for Italian chat-bots

Abstract

This paper illustrates the implementation of Open Unipa-GPT, an open-source version of the Unipa-GPT chat-bot that leverages open-source Large Language Models for embeddings and text generation. The system relies on a Retrieval Augmented Generation approach, thus mitigating hallucination errors in the generation phase. A detailed comparison between different models is reported to illustrate their performance as regards embedding generation, retrieval, and text generation. In the last case, models were tested in a simple inference setup after a fine-tuning procedure. Experiments demonstrate that an open-source LLMs can be efficiently used for embedding generation, but none of the models does reach the performances obtained by closed models, such as gpt-3.5-turbo in generating answers. Corpora and code are available on GitHub

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