1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CL2024★ 1 cited
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data
Younghun Lee, Sungchul Kim, Ryan A. Rossi +2
Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks, yet existing work shows that inference on structured data is challenging…
cs.CL2024
Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
Younghun Lee, Dan Goldwasser, Laura Schwab Reese
Understanding the dynamics of counseling conversations is an important task, yet it is a challenging NLP problem regardless of the recent advance of Transformer-based pre-trained l…
cs.CL2024★ 1 cited
Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models
Younghun Lee, Sungchul Kim, Tong Yu +2
Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is cha…