Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
This work addresses the problem of enhancing emotional comprehension in AI for applications like human-computer interaction, but it is incremental as it builds on existing methods and highlights unresolved issues.
The paper tackled the challenge of modeling empathy in language models using NLP approaches, finding that methods like contrastive learning and supervised fine-tuning showed improvements but overall results remained unsatisfactory, with analysis revealing low annotator agreement and cultural independence in subjectivity.
Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. However, modeling empathy using NLP approaches remains challenging due to its deep interconnection with human interaction dynamics. Previous approaches, which involve fine-tuning language models (LMs) on human-annotated empathic datasets, have had limited success. In our pursuit of improving empathy understanding in LMs, we propose several strategies, including contrastive learning with masked LMs and supervised fine-tuning with large language models. While these methods show improvements over previous methods, the overall results remain unsatisfactory. To better understand this trend, we performed an analysis which reveals a low agreement among annotators. This lack of consensus hinders training and highlights the subjective nature of the task. We also explore the cultural impact on annotations. To study this, we meticulously collected story pairs in Urdu language and find that subjectivity in interpreting empathy among annotators appears to be independent of cultural background. Our systematic exploration of LMs' understanding of empathy reveals substantial opportunities for further investigation in both task formulation and modeling.