A Systematic Approach for Large Language Models Debugging

2026-04-24

AI Summary

This work addresses the challenge of systematically debugging large language models (LLMs), which is hindered by their black-box nature and stochastic behavior, particularly in the absence of standardized benchmarks. The paper introduces the first general-purpose debugging framework for LLMs, treating them as observable systems and establishing a structured pipeline that spans from issue detection to model optimization. The approach integrates model-agnostic evaluation metrics, interpretability techniques, and error analysis tools to enable coordinated iteration over prompts, parameters, and training data. Experimental results demonstrate that the framework substantially improves debugging efficiency, effectively identifying and rectifying model deficiencies across diverse tasks and non-standardized evaluation settings. This enables reproducible, transparent, and scalable model refinement.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Large Language Models
• Debugging
• Error Diagnosis
• Model Transparency
• Probabilistic Nature

Innovation

• systematic debugging
• large language models
• model interpretability
• error analysis
• model-agnostic methodology