9 papers
MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling
Yizhe Yang, Palakorn Achananuparp, Heyan Huang +2
Reasoning large language models (LLMs) have recently made much progress in complex problem-solving, leveraging internal reasoning (or thought) to guide their solution generation. H…
DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories
Neemesh Yadav, Palakorn Achananuparp, Jing Jiang +1
We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human-human dialogues using a multiple-choice evaluation framework. Concurrent with recent…
On Reasoning Behind Next Occupation Recommendation
Shan Dong, Palakorn Achananuparp, Hieu Hien Mai +3
In this work, we develop a novel reasoning approach to enhance the performance of large language models (LLMs) in future occupation prediction. In this approach, a reason generator…
MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models
Suhyun Lee, Palakorn Achananuparp, Neemesh Yadav +2
Large language models (LLMs) are increasingly explored as scalable tools for mental health counseling, yet evaluating their safety remains challenging due to the interactional and…
Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles
Palakorn Achananuparp, Ye Xu, Yao Lu +2
We present a large-scale analysis of career mobility of college-educated U.S. workers using online resume profiles to investigate how gender, race, and job change options are assoc…
A Multi-Stage Framework with Taxonomy-Guided Reasoning for Occupation Classification Using Large Language Models
Palakorn Achananuparp, Ee-Peng Lim, Yao Lu
Automatically annotating job data with standardized occupations from taxonomies, known as occupation classification, is crucial for labor market analysis. However, this task is oft…