A Systematic Approach for Large Language Models Debugging

TL;DR

This paper presents a structured, model-agnostic methodology for debugging large language models, aiming to improve diagnosis, refinement, and transparency in their deployment.

Contribution

It introduces a systematic approach that unifies evaluation, interpretability, and error analysis for effective LLM debugging across diverse tasks.

Findings

Enables iterative diagnosis and refinement of LLMs

Supports debugging without standardized benchmarks

Fosters reproducibility and transparency in LLM deployment

Abstract

Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper introduces a systematic approach for LLM debugging that treats models as observable systems, providing structured, model-agnostic methods from issue detection to model refinement. By unifying evaluation, interpretability, and error-analysis practices, our approach enables practitioners to iteratively diagnose model weaknesses, refine prompts and model parameters, and adapt data for fine-tuning or assessment, while remaining effective in contexts where standardized benchmarks and evaluation criteria are lacking. We argue that such a structured methodology not only accelerates troubleshooting but also fosters reproducibility, transparency, and scalability in the deployment of LLM-based systems.