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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.23027 |
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| _version_ | 1866915956289175552 |
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| author | Shbita, Basel Gentile, Anna Lisa Zhang, Bing An, Sungeun Thakur, Shailja Asthana, Shubhi Zhou, Yi Surendran, Saptha Ahmed, Farhan Kulkarni, Rohan Ong, Yuya Jeremy DeLuca, Chad Patel, Hima |
| author_facet | Shbita, Basel Gentile, Anna Lisa Zhang, Bing An, Sungeun Thakur, Shailja Asthana, Shubhi Zhou, Yi Surendran, Saptha Ahmed, Farhan Kulkarni, Rohan Ong, Yuya Jeremy DeLuca, Chad Patel, Hima |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23027 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Systematic Approach for Large Language Models Debugging Shbita, Basel Gentile, Anna Lisa Zhang, Bing An, Sungeun Thakur, Shailja Asthana, Shubhi Zhou, Yi Surendran, Saptha Ahmed, Farhan Kulkarni, Rohan Ong, Yuya Jeremy DeLuca, Chad Patel, Hima Artificial Intelligence 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. |
| title | A Systematic Approach for Large Language Models Debugging |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2604.23027 |