Computer Science > Computation and Language
arXiv:2603.04857 (cs)
[Submitted on 5 Mar 2026]
Title:FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications
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Abstract:Instruction following is critical for LLMs deployed in enterprise and API-driven settings, where strict adherence to output formats, content constraints, and procedural requirements is essential for enabling reliable LLM-assisted workflows. However, existing instruction following benchmarks predominantly evaluate natural language generation constraints that reflect the needs of chat assistants rather than enterprise users. To bridge this gap, we introduce FireBench, an LLM instruction following benchmark grounded in real-world enterprise and API usage patterns. FireBench evaluates six core capability dimensions across diverse applications including information extraction, customer support, and coding agents, comprising over 2,400 samples. We evaluate 11 LLMs and present key findings on their instruction following behavior in enterprise scenarios. We open-source FireBench at this http URL to help users assess model suitability, support model developers in diagnosing performance, and invite community contributions.
| Subjects: | Computation and Language (cs.CL); Software Engineering (cs.SE) |
| Cite as: | arXiv:2603.04857 [cs.CL] |
| (or arXiv:2603.04857v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.04857
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View a PDF of the paper titled FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications, by Yunfan Zhang and 3 other authors
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