2 citations · 4 across the 10 of their papers we have counts for
8 papers · 1 filter
Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
Sangmin Bae, Yujin Kim, Reza Bayat +8
Scaling language models unlocks impressive capabilities, but the accompanying computational and memory demands make both training and deployment expensive. Existing efficiency effo…
Guiding Reasoning in Small Language Models with LLM Assistance
Yujin Kim, Euiin Yi, Minu Kim +2
The limited reasoning capabilities of small language models (SLMs) cast doubt on their suitability for tasks demanding deep, multi-step logical deduction. This paper introduces a f…
Self-Training Elicits Concise Reasoning in Large Language Models
Tergel Munkhbat, Namgyu Ho, Seo Hyun Kim +3
Chain-of-thought (CoT) reasoning has enabled large language models (LLMs) to utilize additional computation through intermediate tokens to solve complex tasks. However, we posit th…
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…
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…
HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
Yongjin Yang, Joonkee Kim, Yujin Kim +3
With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to id…