arXiv:2601.09001v4 Announce Type: replace Abstract: Deploying LLMs raises two coupled challenges: (1) monitoring--estimating where a model underperforms as traffic and domains drift--and (2) improvement--prioritizing data acquisition to close the largest performance gaps. We test whether an inference-time signal can estimate slice-level accuracy under domain shift. For each response, we compute an output-entropy profile from final-layer next-token probabilities (from top-$k$ logprobs) and summarize it with different statistics. A lightweight classifier predicts instance correctness, and averag
The proliferation of Large Language Models (LLMs) in various applications necessitates robust, continuous monitoring mechanisms to address performance degradation and domain shifts.
This development offers a potential real-time solution for maintaining LLM accuracy and identifying areas for improvement, directly addressing a critical deployment challenge.
The ability to continuously monitor LLM performance at inference time using entropy traces could significantly enhance the reliability and adaptability of deployed AI systems.
- · AI developers
- · Enterprises deploying LLMs
- · AI monitoring software companies
- · Researchers in AI reliability
- · Companies relying on static LLM evaluations
Improved reliability and faster iteration cycles for Large Language Models in production environments.
Reduced operational costs associated with manual LLM monitoring and error detection in complex systems.
Acceleration of sophisticated AI agent deployments in critical applications due to enhanced trust in their continuous performance.
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