179 citations · 362 across the 32 of their papers we have counts for
18 papers · 1 filter
Discourse Diversity in Multi-Turn Empathic Dialogue
Hongli Zhan, Emma S. Gueorguieva, Javier Hernandez +3
Large language models (LLMs) produce responses rated as highly empathic in single-turn settings (Ayers et al., 2023; Lee et al., 2024), yet they are also known to be formulaic gene…
AI generates well-liked but templatic empathic responses
Emma S. Gueorguieva, Hongli Zhan, Jina Suh +4
Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than hum…
Verbalizing LLMs' assumptions to explain and control sycophancy
Myra Cheng, Isabel Sieh, Humishka Zope +7
LLMs can be socially sycophantic, affirming users when they ask questions like "am I in the wrong?" rather than providing genuine assessment. We hypothesize that this behavior aris…
Characterizing Delusional Spirals through Human-LLM Chat Logs
Jared Moore, Ashish Mehta, William Agnew +11
As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological effects, such as delusions, self-harm, and ``AI psychosis,'' have emerged…
HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue
Laya Iyer, Kriti Aggarwal, Sanmi Koyejo +3
Supportive conversation depends on skills that go beyond language fluency, including reading emotions, adjusting tone, and navigating moments of resistance, frustration, or distres…
Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
Jared Moore, Declan Grabb, William Agnew +4
Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech star…
Modeling Subjectivity in Cognitive Appraisal with Language Models
Yuxiang Zhou, Hainiu Xu, Desmond C. Ong +3
As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tas…
Human-like Affective Cognition in Foundation Models
Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken +5
Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a va…
Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided
Hongli Zhan, Allen Zheng, Yoon Kyung Lee +3
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However…
Large Language Models Produce Responses Perceived to be Empathic
Yoon Kyung Lee, Jina Suh, Hongli Zhan +2
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate…
Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models
Hongli Zhan, Desmond C. Ong, Junyi Jessy Li
The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations su…
ReaSCAN: Compositional Reasoning in Language Grounding
Zhengxuan Wu, Elisa Kreiss, Desmond C. Ong +1
The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, Ne…
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
Varsha Suresh, Desmond C. Ong
Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensi…
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition
Varsha Suresh, Desmond C. Ong
Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in…
On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification
Zhengxuan Wu, Desmond C. Ong
BERT, as one of the pretrianed language models, attracts the most attention in recent years for creating new benchmarks across GLUE tasks via fine-tuning. One pressing issue is to…
Structured Self-Attention Weights Encode Semantics in Sentiment Analysis
Zhengxuan Wu, Thanh-Son Nguyen, Desmond C. Ong
Neural attention, especially the self-attention made popular by the Transformer, has become the workhorse of state-of-the-art natural language processing (NLP) models. Very recent…
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
Zhengxuan Wu, Desmond C. Ong
Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a…
A Multimodal LSTM for Predicting Listener Empathic Responses Over Time
Zhi-Xuan Tan, Arushi Goel, Thanh-Son Nguyen +1
People naturally understand the emotions of-and often also empathize with-those around them. In this paper, we predict the emotional valence of an empathic listener over time as th…