Blackbox Performance Analysis of LLM-based Network Control
However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems.
ProofPaper ↗
Key points
- LLMs are becoming an indispensable part of network control systems due to their impressive reasoning.
- Before such LLM-enabled systems can be deployed, it is imperative to develop tools that can audit their performance on previously unseen inputs.
- It uses novel methods to extend deep reinforcement learning to guide the search for performance gaps and inputs that make LLMs struggle.
- LENS finds large gaps for LLM-based traffic engineering and GPU allocation systems, and even matches existing white-box analyzers designed for hand-crafted TE heuristics.
Sources (1)
- [1]Blackbox Performance Analysis of LLM-based Network ControlarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 12:35 AM
However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems.
LLMs are becoming an indispensable part of network control systems due to their impressive reasoning.
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