Towards Blind and Low-Vision Accessibility of Lightweight VLMs and Custom LLM-Evals

The exploration of accessibility for blind and low-vision users through lightweight vision-language models (VLMs) is crucial, especially given the high demands of current models. The study on SmolVLM2 variants highlights the impact of model size on description quality, resonating with findings in related works that evaluate the functional properties of large language models (LLMs) in various contexts. For instance, the article on penetration testing emphasizes the need for reliable performance across different scenarios, which parallels the necessity for robust accessibility frameworks like the Multi-Context BLV Framework. These frameworks aim to enhance user experience by providing detailed, context-aware descriptions, a theme echoed in the ongoing research on LLMs and their applications.