arXiv:2602.12966v2 Announce Type: replace Abstract: Understanding how and why large language models (LLMs) fail is becoming a central challenge as models rapidly evolve and static evaluations fall behind. While automated probing has been enabled by dynamic test generation, existing approaches often discover isolated failure cases, lack principled control over exploration, and provide limited insight into the underlying structure of model weaknesses. We propose ProbeLLM, a benchmark-agnostic automated probing framework that elevates weakness discovery from individual failures to structured fail
As large language models rapidly evolve and static evaluations prove insufficient, there is an urgent need for dynamic and principled methods to diagnose and understand their failure modes.
This work is critical for improving the reliability, safety, and trustworthiness of AI systems by enabling a systematic approach to identifying and addressing their weaknesses.
The ability to dynamically and systematically diagnose LLM failures moves beyond isolated bug fixes to a more structured understanding of model weaknesses, paving the way for more robust and predictable AI.
- · AI developers
- · AI safety researchers
- · Enterprises deploying LLMs
- · Open-source AI frameworks
- · AI models with opaque failure modes
- · Developers relying solely on static benchmarks
More robust and reliable large language models become technically feasible.
Increased adoption of LLMs in critical applications due to enhanced trustworthiness and predictable performance.
Accelerated development of general-purpose AI as foundational models become inherently more debuggable and auditable.
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Read at arXiv cs.CL