2 citations · 4 across the 11 of their papers we have counts for
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cs.AI2024
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Gihun Lee, Minchan Jeong, Yujin Kim +4
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents furthe…
cs.CL2024★ 2 cited
TnT-LLM: Text Mining at Scale with Large Language Models
Mengting Wan, Tara Safavi, Sujay Kumar Jauhar +11
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and applicati…
cs.CL2024★ 2 cited
Leveraging Large Language Models for Hybrid Workplace Decision Support
Yujin Kim, Chin-Chia Hsu
Large Language Models (LLMs) hold the potential to perform a variety of text processing tasks and provide textual explanations for proposed actions or decisions. In the era of hybr…