Conversation disentanglement is the process of segmenting a stream of messages or utterances into separate conversations or "threads" that can be more easily understood and processed. We compare the performance of GPT-4o and GPT-4o Mini with deep learning models built from scratch for this task. We show that, using the same amount of training data, out-of-the-box GPT-4o performs poorly, and fine-tuning GPT-4o Mini results in performance comparable to learning small-size models from scratch (based on standard hand-crafted features for this task), with performance reaching 74.4% F1-score for prediction of links between messages and 45.3% F1-score for prediction of perfectly matching conversations. However, the fine-tuned GPT-4o Mini model underperforms when compared to models that utilize complex structural information. We also provide a new method for detailed analysis of the successes and failures of our models, and a new visualization method.

Dialogue systems often rely on overly simplistic persona representations, limiting their capacity to portray realistic, nuanced characters. In this paper, we explore how well existing persona-grounding methods capture complex personalities using two character-rich domains—Sgt Blackwell (single-character) and Twins (two-character)—described extensively through detailed narratives. We compare early fusion techniques, Retrieval-Augmented Generation (RAG), and relevance-based approaches. Evaluations across entailment, persona alignment, and hallucination metrics reveal distinct trade-offs: Knowledge Graph fusion notably reduces hallucinations and maintains relevance, Persona fusion strongly preserves relevance but has higher hallucination rates, and RAG provides fast, fluent responses. Our findings emphasize the critical role of structured persona grounding in achieving nuanced personality modeling.