LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to three structural problems: conflicting gradients that cancel, static data selection that cannot track evolving learning dynamics, and subspace saturation that causes later up...
Hong-Yu Cao, Yan-Chi Liu, Kun-Peng Liu et al.· 0 citations
Diagram-to-graph topology extraction aims to extract a graph of entities and their connections from a structural diagram. This task remains challenging for current vision-language models because it requires both fine-grained perceptual grounding and topology-aware reasoning with global consistency. We present TopoBench...
Bang-Wei Guo, Xujiang Zhao, Yan-Chi Liu et al.· 0 citations
Uncertainty reasoning and quantification play a critical role in decision-making across various domains, prompting increased attention from both academia and industry. As real-world applications become more complex and data-driven, effectively handling uncertainty becomes paramount for accurate and reliable decision-ma...
Xujiang Zhao, Chen Zhao, Feng Chen et al.· Proceedings of the 32nd ACM...· 0 citations
UMCTS is an Uncertainty-aware Monte Carlo Tree Search framework that combines the language understanding capability of large language models with the reliability of well-established solvers and achieves state-of-the-art solution accuracy and improves efficiency by reducing token usage.
Linlin Yu, Xujiang Zhao, Dong Li et al.· Annual Meeting of the Associ...· 0 citations
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