Conversational AI is a central application of NLP, yet ensuring high response quality remains challenging due to the inherently subjective nature of user satisfaction. Dialogue evaluation can be performed manually—through expert or user ratings—or automatically, using methods that aim to predict quality scores consistent with human judgment. In this work, we present a reference-free automatic dialogue evaluation system that predicts user ratings from a dataset of real human–chatbot interactions collected during the Alexa Prize Socialbot Grand Challenge 5, combining multiple complementary models to enhance correlation with human scores. Experimental results indicate that the model that achieves the highest Pearson correlation with users’ ratings is an XGBoost regression model that combines different features such as conversation length, engineered flags capturing conversation characteristics, predictions from an Encoder-based Panel of Experts (PoE), and instruction-based outputs from a fine-tuned LLM. The overall Pearson Correlation on the eval set is 0.404, which is competitive with prior work trained on an order of magnitude more dialogues, albeit using different datasets and system configurations.

Recent studies suggest that increasing the context window of language models could outperform retrieval-augmented generation (RAG) methods in certain tasks. However, in domains such as art and museums, where information is inherently multimodal, combining images and detailed textual descriptions, this assumption needs closer examination. To explore this, we compare RAG techniques with direct large-context input approaches for answering questions about artworks. Using a dataset of painting images paired with textual information, we develop a synthetic database of question-answer (QA) pairs for evaluating these methods. The focus is on assessing the efficiency and accuracy of RAG in retrieving and using relevant information compared to passing the entire textual context to a language model. Additionally, we experiment with various strategies for segmenting and retrieving text to optimise the RAG pipeline. The results aim to clarify the trade-offs between these approaches and provide valuable insights for interactive systems designed for art and museum contexts.