Instruction-Following Evaluation in Function Calling for Large Language
  Models
Authors: name: Nikolai Skripko

Function calling is a core capability of large language models, essential for
AI agents. Existing benchmarks such as the Berkeley Function Calling
Leaderboard (BFCL), tau^2-Bench (arXiv:2506.07982), and ACEBench
(arXiv:2501.12851) evaluate argument correctness but do not test adherence to
format instructions embedded in parameter descriptions, such as enclosing
values in double quotes or using ISO date formats.
  We introduce IFEval-FC, a benchmark inspired by IFEval (arXiv:2311.07911)
that assesses precise instruction following in function calling. IFEval-FC
encodes verifiable formats directly within JSON schema descriptions, for
example specifying that a value must not contain punctuation. It includes 750
test cases, each consisting of a function with an embedded format for one of
its input parameters and a corresponding user query. Evaluation is fully
algorithmic, ensuring objectivity, reproducibility, and scalability.
  Our results show that even state-of-the-art proprietary models, including
GPT-5 and Claude 4.1 Opus, frequently fail to follow basic formatting rules,
highlighting a practical limitation for real-world agent systems. The complete
codebase and data are publicly available at
https://github.com/Skripkon/IFEval-FC.