Papers by Lina M. Rojas-Barahona
Evaluating Conversational Agents with Persona-driven User Simulations based on Large Language Models: A Sales Bot Case Study (2025.emnlp-industry)
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Justyna Gromada, Alicja Kasicka, Ewa Komkowska, Lukasz Krajewski, Natalia Krawczyk, Morgan Veyret, Bartosz Przybył, Lina M. Rojas-Barahona, Michał K. Szczerbak
| Challenge: | Recent advances in LLMs enable sophisticated user simulations that can replace traditional rule-based evaluations. |
| Approach: | They propose a persona-driven approach to conversational agent evaluation using Large Language Models (LLMs) they introduce a dataset of customer personas, which are then used to configure a single LLM-based user simulator. |
| Outcome: | The proposed model emulates nuanced customer roles and can implement cross-selling strategies with minimal impact on customer satisfaction, varying by customer type. |
Feudal Reinforcement Learning for Dialogue Management in Large Domains (N18-2)
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Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Stefan Ultes, Lina M. Rojas-Barahona, Bo-Hsiang Tseng, Milica Gašić
| Challenge: | Reinforcement learning (RL) is a promising approach to model dialogue policy optimisation but fails to scale to large domains due to the curse of dimensionality. |
| Approach: | They propose a novel approach to dialogue policy optimisation using reinforcement learning . they propose to decompose the decision into two steps using a domain ontology . |
| Outcome: | The proposed architecture outperforms state-of-the-art in several dialogue domains without any additional reward signal. |