Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question
  Answering with LLMs
Authors: name: Mario Sanz-Guerrero, name: Minh Duc Bui, name: Katharina von der Wense

When evaluating large language models (LLMs) with multiple-choice question
answering (MCQA), it is common to end the prompt with the string "Answer:" to
facilitate automated answer extraction via next-token probabilities. However,
there is no consensus on how to tokenize the space following the colon, often
overlooked as a trivial choice. In this paper, we uncover accuracy differences
of up to 11% due to this (seemingly irrelevant) tokenization variation as well
as reshuffled model rankings, raising concerns about the reliability of LLM
comparisons in prior work. Surprisingly, we are able to recommend one specific
strategy -- tokenizing the space together with the answer letter -- as we
observe consistent and statistically significant performance improvements.
Additionally, it improves model calibration, enhancing the reliability of the
model's confidence estimates. Our findings underscore the importance of careful
evaluation design and highlight the need for standardized, transparent
evaluation protocols to ensure reliable and comparable results.