FireBench: Evaluating Instruction Following in Enterprise and API-Driven LLM Applications
arXiv:2603.04857v1 Announce Type: new 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 fire-bench.com to help users assess model suitability, support m
arXiv:2603.04857v1 Announce Type: new 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 fire-bench.com to help users assess model suitability, support model developers in diagnosing performance, and invite community contributions.
Executive Summary
The article introduces FireBench, a benchmark for evaluating instruction following in enterprise and API-driven large language models (LLMs). FireBench assesses six core capability dimensions across various applications, including information extraction and coding agents, and provides key findings on the instruction following behavior of 11 LLMs. The benchmark aims to bridge the gap between existing natural language generation constraints and the needs of enterprise users, enabling reliable LLM-assisted workflows. By open-sourcing FireBench, the authors invite community contributions and support model developers in diagnosing performance.
Key Points
- ▸ FireBench is a benchmark for evaluating instruction following in enterprise and API-driven LLMs
- ▸ The benchmark assesses six core capability dimensions across diverse applications
- ▸ FireBench provides key findings on the instruction following behavior of 11 LLMs
Merits
Comprehensive Evaluation
FireBench provides a thorough assessment of LLMs' instruction following capabilities, covering various applications and scenarios.
Demerits
Limited Generalizability
The benchmark's focus on enterprise and API-driven settings may limit its generalizability to other domains or applications.
Expert Commentary
The introduction of FireBench represents a significant step forward in evaluating the instruction following capabilities of LLMs in enterprise and API-driven settings. By providing a comprehensive assessment of LLMs' ability to adhere to output formats, content constraints, and procedural requirements, FireBench can help users identify the most suitable models for their specific use cases. Furthermore, the benchmark's findings can inform the development of more effective and reliable LLM-assisted workflows, ultimately driving greater adoption and integration of LLMs in enterprise settings.
Recommendations
- ✓ Model developers should utilize FireBench to diagnose and improve their models' instruction following performance
- ✓ Enterprise users should leverage FireBench to assess the suitability of LLMs for their specific applications and use cases