Sunghwan Mac Kim

Also published as: Sunghwan Kim


2024

pdf bib
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
Dongjin Kang | Sunghwan Kim | Taeyoon Kwon | Seungjun Moon | Hyunsouk Cho | Youngjae Yu | Dongha Lee | Jinyoung Yeo
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Emotional Support Conversation (ESC) is a task aimed at alleviating individuals’ emotional distress through daily conversation. Given its inherent complexity and non-intuitive nature, ESConv dataset incorporates support strategies to facilitate the generation of appropriate responses. Recently, despite the remarkable conversational ability of large language models (LLMs), previous studies have suggested that they often struggle with providing useful emotional support. Hence, this work initially analyzes the results of LLMs on ESConv, revealing challenges in selecting the correct strategy and a notable preference for a specific strategy. Motivated by these, we explore the impact of the inherent preference in LLMs on providing emotional support, and consequently, we observe that exhibiting high preference for specific strategies hinders effective emotional support, aggravating its robustness in predicting the appropriate strategy. Moreover, we conduct a methodological study to offer insights into the necessary approaches for LLMs to serve as proficient emotional supporters. Our findings emphasize that (1) low preference for specific strategies hinders the progress of emotional support, (2) external assistance helps reduce preference bias, and (3) existing LLMs alone cannot become good emotional supporters. These insights suggest promising avenues for future research to enhance the emotional intelligence of LLMs.

2018

pdf bib
Proceedings of the Australasian Language Technology Association Workshop 2018
Sunghwan Mac Kim | Xiuzhen (Jenny) Zhang
Proceedings of the Australasian Language Technology Association Workshop 2018

2017

pdf bib
Demographic Inference on Twitter using Recursive Neural Networks
Sunghwan Mac Kim | Qiongkai Xu | Lizhen Qu | Stephen Wan | Cécile Paris
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In social media, demographic inference is a critical task in order to gain a better understanding of a cohort and to facilitate interacting with one’s audience. Most previous work has made independence assumptions over topological, textual and label information on social networks. In this work, we employ recursive neural networks to break down these independence assumptions to obtain inference about demographic characteristics on Twitter. We show that our model performs better than existing models including the state-of-the-art.

2016

pdf bib
Data61-CSIRO systems at the CLPsych 2016 Shared Task
Sunghwan Mac Kim | Yufei Wang | Stephen Wan | Cécile Paris
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

pdf bib
The Effects of Data Collection Methods in Twitter
Sunghwan Mac Kim | Stephen Wan | Cécile Paris | Brian Jin | Bella Robinson
Proceedings of the First Workshop on NLP and Computational Social Science

pdf bib
Detecting Social Roles in Twitter
Sunghwan Mac Kim | Stephen Wan | Cécile Paris
Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media

2015

pdf bib
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers
Sunghwan Mac Kim | Steve Cassidy
Proceedings of the Australasian Language Technology Association Workshop 2015

2014

pdf bib
The Effect of Dependency Representation Scheme on Syntactic Language Modelling
Sunghwan Kim | John Pate | Mark Johnson
Proceedings of the Australasian Language Technology Association Workshop 2014

2012

pdf bib
Improving Combinatory Categorial Grammar Parse Reranking with Dependency Grammar Features
Sunghwan Mac Kim | Dominick Ng | Mark Johnson | James Curran
Proceedings of COLING 2012

2010

pdf bib
Evaluation of Unsupervised Emotion Models to Textual Affect Recognition
Sunghwan Mac Kim | Alessandro Valitutti | Rafael A. Calvo
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text