From the 1 of 5 linked papers with an AI index.
5 papers
Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques
Daehoon Gwak, Minhyung Lee, Junwoo Park +1
The paper surveys methods for speeding up inference of masked diffusion large language models by categorizing algorithmic, architectural, and system-level acceleration techniques a…
Not the Example, but the Process: How Self-Generated Examples Enhance LLM Reasoning
Daehoon Gwak, Minseo Jung, Junwoo Park +4
Recent studies have shown that Large Language Models (LLMs) can improve their reasoning performance through self-generated few-shot examples, achieving results comparable to manual…
Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs
Daehoon Gwak, Minseo Jung, Junwoo Park +4
Masked diffusion models (MDMs) offer a promising non-autoregressive alternative for large language modeling. Standard decoding methods for MDMs, such as confidence-based sampling,…
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
Junwoo Park, Hyuck Lee, Dohyun Lee +2
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecastin…
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
Daehoon Gwak, Junwoo Park, Minho Park +4
Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and g…