5 accepted papers
Fine-tuning Large Language Models (LLMs) as autonomous agents on domain-specific data has emerged as a promising paradigm for tackling interactive, real-world tasks. However, existing studies have overlooked the critical coordination between long-term planning and multi-step execution in optimizing …
LaTeX provides precise representation of complex elements (i.e., tables and equations) in scientific documents. However, the automated transcription of visual representations into LaTeX code is challenging and prone to errors. This paper introduces LaTeXNet, a specialized model designed to automate…
Most existing symbolic music generation methods focus on generating short pieces, typically less than 8 bars and occasionally up to 32 bars. Generating long music sequences requires effective representation of coherent musical structures. Vanilla self-attention face challenges in capturing subtle lo…
Pre-trained large language models exhibit significant potential in speech and language processing. Fine-tuning all parameters becomes impractical when confronted with numerous downstream tasks. To address this challenge, various low-rank adaptation techniques have been introduced for parameter-effic…
Graph matching has a wide spectrum of real-world applications and in general is known NP-hard. In many vision tasks, one realistic problem arises for finding the global node mappings across a batch of corrupted weighted graphs. This paper is an attempt to connect graph matching, especially multi-gra…