The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple,
  Self-Contained Directives
Authors: name: Henry Lim, name: Kwan Hui Lim

Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot
reasoning, yet their ability to execute simple, self-contained instructions
remains underexplored, despite this being foundational to complex
instruction-following. We evaluate 20 IT-LLMs on modified MMLU and MMLU-Pro
benchmarks, by systematically varying the format of option labels (alphabetic,
numeric, Roman) while keeping their meaning identical under four paradigms,
namely: (1) With explicit instructions, label changes cause large performance
shifts (e.g., -30.45\% for Roman vs. numeric), revealing instruction-format
bias. (2) Without instructions, performance drops further (up to -10.84\%) and
label sensitivity intensifies, underscoring the role of explicit guidance. (3)
When option contents are removed, models fail random-choice baselines except
with numeric labels, suggesting weak adherence to atomic directives. (4)
Three-shot exemplars yield no significant gains in robustness or fidelity, and
generation analyses show persistent label errors, especially for non-numeric
formats. Across model sizes, larger LLMs achieve higher accuracy but remain
inconsistent in instruction adherence. These results expose the insufficiencies
of current instruction-tuning paradigms and highlight the need for evaluation
methods and training strategies that explicitly target atomic
instruction-following.