Affect Recognition in Conversations Using Large Language Models

Affect recognition, encompassing emotions, moods, and feelings, plays a pivotal role in human communication. In the realm of conversational artificial intelligence (AI), the ability to discern and respond to human affective cues is a critical factor for creating engaging and empathetic interactions. This study delves into the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP, EmoWOZ, and DAIC-WOZ, covering a spectrum of dialogues from casual conversations to clinical interviews, we evaluated and compared LLMs' performance in affect recognition. Our investigation explores the zero-shot and few-shot capabilities of LLMs through in-context learning (ICL) as well as their model capacities through task-specific fine-tuning. Additionally, this study takes into account the potential impact of automatic speech recognition (ASR) errors on LLM predictions. With this work, we aim to shed light on the extent to which LLMs can replicate human-like affect recognition capabilities in conversations

Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system

International audienceThe optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task

Supplementary data for the article: Fotirić Akšić, M.; Dabić Zagorac, D.; Sredojević, M.; Milivojević, J.; Gašić, U.; Meland, M.; Natić, M. Chemometric Characterization of Strawberries and Blueberries According to Their Phenolic Profile: Combined Effect of Cultivar and Cultivation System. Molecules (Basel, Switzerland) 2019, 24 (23). https://doi.org/10.3390/molecules24234310

Supplementary material for: [https://doi.org/10.3390/molecules24234310 ]Related to published version: [http://cherry.chem.bg.ac.rs/handle/123456789/3747