Andrea Martinenghi

Università degli Studi di Milano-Bicocca | UNIMIB · Department of Psychology

LLM-based chatbots represent a significant milestone as the initial point of interaction between artificial intelligence and the general public. These chatbots offer greater flexibility compared to traditional chatbots, yet their behavior deviates notably from human interaction patterns. Current annotation schemas may not be adequately suited to ca...

Can a LLM understand dialogues, in particular from multi-party settings? In this study we explore this question through the pragmatic framework of Dialogue Acts or Speech Acts. Two tasks - classification and prediction of Dialogue Acts an - are tested with zero and few-shot learning on the STAC multi-party corpus that contains over 13000 EDUs (Ele...

Human language interactions involve complex processes beyond pure information exchange, for example, actions aimed at influencing beliefs and behaviors within a communicative context. In this paper, we propose to investigate the dialogue understanding capabilities of large language models (LLMs), particularly in multi-party settings, where challeng...