  Large vision-language models (LVLMs), designed to interpret and respond to
human instructions, occasionally generate hallucinated or harmful content due
to inappropriate instructions. This study uses linear probing to shed light on
the hidden knowledge at the output layers of LVLMs. We demonstrate that the
logit distributions of the first tokens contain sufficient information to
determine whether to respond to the instructions, including recognizing
unanswerable visual questions, defending against jailbreaking attacks, and
identifying deceptive questions. Such hidden knowledge is gradually lost in
logits of subsequent tokens during response generation. Then, we illustrate a
simple decoding strategy at the generation of the first token, effectively
improving the generated content. In experiments, we find a few interesting
insights: First, the CLIP model already contains a strong signal for solving
these tasks, which indicates potential bias in the existing datasets. Second,
we observe performance improvement by utilizing the first logit distributions
on three additional tasks, including indicating uncertainty in math solving,
mitigating hallucination, and image classification. Last, with the same
training data, simply finetuning LVLMs improves models' performance but is
still inferior to linear probing on these tasks.
