  Visually Impaired Assistance (VIA) aims to automatically help the visually
impaired (VI) handle daily activities. The advancement of VIA primarily depends
on developments in Computer Vision (CV) and Natural Language Processing (NLP),
both of which exhibit cutting-edge paradigms with large models (LMs).
Furthermore, LMs have shown exceptional multimodal abilities to tackle
challenging physically-grounded tasks such as embodied robots. To investigate
the potential and limitations of state-of-the-art (SOTA) LMs' capabilities in
VIA applications, we present an extensive study for the task of VIA with LMs
(VIALM). In this task, given an image illustrating the physical environments
and a linguistic request from a VI user, VIALM aims to output step-by-step
guidance to assist the VI user in fulfilling the request grounded in the
environment. The study consists of a survey reviewing recent LM research and
benchmark experiments examining selected LMs' capabilities in VIA. The results
indicate that while LMs can potentially benefit VIA, their output cannot be
well environment-grounded (i.e., 25.7% GPT-4's responses) and lacks
fine-grained guidance (i.e., 32.1% GPT-4's responses).
