11 citations · 47 across the 26 of their papers we have counts for
6 papers · 1 filter
Position: Thematic Analysis of Unstructured Clinical Transcripts with Large Language Models
Seungjun Yi, Joakim Nguyen, Terence Lim +8
This position paper examines how large language models (LLMs) can support thematic analysis of unstructured clinical transcripts, a widely used but resource-intensive method for un…
SFT-TA: Supervised Fine-Tuned Agents in Multi-Agent LLMs for Automated Inductive Thematic Analysis
Seungjun Yi, Joakim Nguyen, Huimin Xu +8
Thematic Analysis (TA) is a widely used qualitative method that provides a structured yet flexible framework for identifying and reporting patterns in clinical interview transcript…
Teaching with Lies: Curriculum DPO on Synthetic Negatives for Hallucination Detection
Shrey Pandit, Ashwin Vinod, Liu Leqi +1
Aligning large language models (LLMs) to accurately detect hallucinations remains a significant challenge due to the sophisticated nature of hallucinated text. Recognizing that hal…
Learning Composable Chains-of-Thought
Fangcong Yin, Zeyu Leo Liu, Liu Leqi +2
A common approach for teaching large language models (LLMs) to reason is to train on chain-of-thought (CoT) traces of in-distribution reasoning problems, but such annotated data is…
Prompting Fairness: Integrating Causality to Debias Large Language Models
Jingling Li, Zeyu Tang, Xiaoyu Liu +4
Large language models (LLMs), despite their remarkable capabilities, are susceptible to generating biased and discriminatory responses. As LLMs increasingly influence high-stakes d…
Many Ways to Be Lonely: Fine-Grained Characterization of Loneliness and Its Potential Changes in COVID-19
Yueyi Jiang, Yunfan Jiang, Liu Leqi +1
Loneliness has been associated with negative outcomes for physical and mental health. Understanding how people express and cope with various forms of loneliness is critical for ear…