  The open-ended nature of language generation makes the evaluation of
autoregressive large language models (LLMs) challenging. One common evaluation
approach uses multiple-choice questions (MCQ) to limit the response space. The
model is then evaluated by ranking the candidate answers by the log probability
of the first token prediction. However, first-tokens may not consistently
reflect the final response output, due to model's diverse response styles such
as starting with "Sure" or refusing to answer. Consequently, MCQ evaluation is
not indicative of model behaviour when interacting with users. But by how much?
We evaluate how aligned first-token evaluation is with the text output along
several dimensions, namely final option choice, refusal rate, choice
distribution and robustness under prompt perturbation. Our results show that
the two approaches are severely misaligned on all dimensions, reaching mismatch
rates over 60%. Models heavily fine-tuned on conversational or safety data are
especially impacted. Crucially, models remain misaligned even when we
increasingly constrain prompts, i.e., force them to start with an option letter
or example template. Our findings i) underscore the importance of inspecting
the text output as well and ii) caution against relying solely on first-token
evaluation.
