Cross-modal skill injection enhances VLM capabilities efficiently

Researchers have explored a technique called cross-modal skill injection to efficiently transfer domain-specific expertise from large language models (LLMs) to vision-language models (VLMs). This method aims to induce new cross-modal capabilities without requiring extensive new training data or significant computational resources, unlike traditional fine-tuning. The study found that this skill injection is effective for instruction-following and cross-lingual tasks but less so for mathematical reasoning. Among tested methods, TA and DARE proved superior, with the research also providing a detailed analysis of their critical hyperparameter tuning. AI

影响 Introduces a more efficient method for adapting existing models to new domains, potentially reducing development costs and time.

排序理由 Academic paper detailing a novel method for enhancing model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

• Large Language Models
• Vision-Language Models

• 论文
• 模型发布

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →