5 papers · 1 filter
Grid-Bench: Can LLMs Predict How Discrete Worlds Change?
Zixiang Xu, Zirui Song, Wanrong Zheng +16
Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, each acts on a configuration sh…
Exploring the Design Space of Permutation Diffusion LLMs
Zhe Xu, Mingfu Liang, Kavosh Asadi +16
Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study this problem…
Self-Guided Test-Time Training for Long-Context LLMs
Xinyu Zhu, Zhe Xu, Xiaohan Wei +10
Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long…
Learning Evidence Highlighting for Frozen LLMs
Shaoang Li, Yanhang Shi, Yufei Li +10
Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts. We introduce HiLight, an Evidence Emphasis framework that…
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification
Bo-Ru Lu, Frank Shyu, Yun-Nung Chen +2
Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC in…