## 2 Related Work  
The existing research dealing with open-domain chatbots primarily targets two avenues: the development of more advanced neural generation methods [3; 4] (often achieved by integrating knowledge bases into generative models); and the established methodologies that typically rely on dialogue trees ([5]). An exemplar of the latter approach can be found in the architectural blueprint proposed by [6]. A major drawback of traditional dialogue tree systems is their limited scope. These systems rely heavily on predefined rules and scripts, which restrict their ability to handle the vast array of possible user inputs and conversational contexts. This rigidity often results in unnatural and repetitive interactions as the system cannot adapt to new or unexpected user queries. Additionally, the manual effort required to design and update these dialogue trees is considerable, making them less scalable for complex applications. To address these limitations, several researchers have sought to enhance these predetermined rule systems by integrating statistical generative models [2; 7]. Traditional generative models, like n-grams, have been replaced by more advanced neural network architectures as computational power has increased. Typically, these newer models use encoder-decoder networks [8] with LSTM [9] or other recurrent units. While these models have significantly improved language modeling and other NLP tasks, their design does not allow for high parallelization, thus limiting their size. A major breakthrough came with the introduction of the Transformer architecture [10], which forms the basis of most current state-of-the-art language models. This led to the development of new static models, such as BERT [11] and ALBERT [12], as well as advanced generative models, such as GPT [13] or T5 [14]. The prior state-of-the-art generative models, such as DialogueGPT [15] and T5 [14], had limited capacity to manage extensive, in-depth conversations. These models often struggled with maintaining context over long dialogues, leading to issues such as generating hallucinations--responses that are plausible-sounding but factually incorrect--and contradictions within the conversation. Furthermore, these models require substantial computational resources for training and deployment, which poses additional challenges for practical implementation. Hence, [2] decided to use traditional, manually created dialogues by professionals and to use their own version of DialogueGPT for out-of-domain inputs. In November 2022, OpenAI ([[https://openai.com/](https://openai.com/)]([https://openai.com/](https://openai.com/)), accessed on 2 December 2015) unveiled GPT3.5 and, subsequently, introduced GPT4. These two models represented a significant step forward for the natural language processing (NLP) field and effectively initiated a new phase in the development of highly competent instruction-based large language models. The Llama model proposed by [16] and the Falcon LLM proposed by [17] can be employed in open-domain chatbots either as standalone systems (such as Chatbotaena ([[https://stablediffusion.fr/vicuna](https://stablediffusion.fr/vicuna)]([https://stablediffusion.fr/vicuna](https://stablediffusion.fr/vicuna)), accessed on 27 May 2023) or as part of an integrated pipeline. More recently, FastChat ([[https://github.com/lm-sys/FastCh](https://github.com/lm-sys/FastCh)]([https://github.com/lm-sys/FastCh](https://github.com/lm-sys/FastCh)) at, accessed on 30 March 2023) introduced the Vicuna model, a fine-tuned version of the Llama model optimized for an instruction-based dataset. In our study, we used the 7 billion-parameter Vicuna model, a 3 billion-parameter version of BlenderBot 3, and two other smaller language models that were integrated into our Barista NRG (DeBERTa-xsmall [11; 18]) for classification, as well as FLAN-T5 [19] for query generation when external information was required.