A Systematic Approach for Large Language Models Debugging
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 prompt...
| Authors | Basel Shbita, Anna Lisa Gentile, Bing Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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