AGENTIC-SYSTEMSApril 28, 20261 min read7 views

A Systematic Approach for Large Language Models Debugging

This paper introduces a systematic, model-agnostic approach for LLM debugging by treating models as observable systems to improve diagnostic efficiency and scalability.

Computer Science > Artificial Intelligence

Title:A Systematic Approach for Large Language Models Debugging

View PDF HTML (experimental)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.

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Source: arXiv cs.AI Recent

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