Enterprise AI Analysis

Abstract Summary

Large Language Models (LLM) benchmarks tell us when models fail, but not why they fail. A wrong answer on a reasoning dataset may stem from formatting issues, calculation errors, or dataset noise rather than weak reasoning. Without disentangling such causes, benchmarks remain incomplete and cannot reliably guide model improvement. We introduce ErrorMap, the first method to chart the sources of LLM failure. It extracts a model's unique "failure signature", clarifies what benchmarks measure, and broadens error identification to reduce blind spots. This helps developers debug models, aligns benchmark goals with outcomes, and supports informed model selection. ErrorMap works on any model or dataset with the same logic. Applying our method to 35 datasets and 83 models we generate ErrorAtlas, a taxonomy of model errors, revealing recurring failure patterns. ErrorAtlas highlights error types that are currently underexplored in LLM research, such as omissions of required details in the output and question misinterpretation. By shifting focus from where models succeed to why they fail, ErrorMap and ErrorAtlas enable advanced evaluation one that exposes hidden weaknesses and directs progress. Unlike success, typically measured by task-level metrics, our approach introduces a deeper evaluation layer that can be applied globally across models and tasks, offering richer insights into model behavior and limitations. We make the taxonomy and code publicly available, with plans to periodically update ErrorAtlas as new benchmarks and models emerge.

Key Findings for Enterprise AI

• ErrorMap: LLM-based Error Analysis: ErrorMap provides a novel LLM-based technique to generate a dedicated, interpretable taxonomy of LLM errors, applicable across diverse domains and input formats, enhancing model comparison and debugging capabilities.
• ErrorAtlas: Comprehensive Static Taxonomy: Generated using ErrorMap on 83 models and 35 datasets, ErrorAtlas offers a static, cross-field taxonomy of common LLM failure modes, revealing underlying model limitations and enabling consistent comparisons of weaknesses over time.
• Identification of Understudied Errors: The analysis highlights prevalent but understudied error types such as "Missing Required Element" (omissions of details, incomplete answers) and "Specification Misinterpretation" (misunderstanding question intent or context), crucial for improving AI reliability.
• Nuanced Model Behavioral Profiling: ErrorMap and ErrorAtlas demonstrate that different model versions, types, and families exhibit distinct error patterns, enabling targeted evaluation, comparison, and debugging strategies for model developers and benchmark curators.
• Public Availability and Continuous Improvement: The taxonomy and code are publicly released, with plans for periodic updates, ensuring the resource evolves with new benchmarks and models to continuously support advanced evaluation.