Sequential statistical inference for Large Language Models: Representation, validity, and monitoring
arXiv:2606.07624v1 Announce Type: new Abstract: This discussion argues that sequential statistical inference can naturally contribute to LLM trustworthiness. In deployment, LLM systems are queried repeatedly, conditioned on evolving contexts, and incorporate user or tool feedback, and may exhibit behavioral shifts after model updates or distribution changes. The discussion is organized around three tasks: representation, modeling LLM interactions as dependent stochastic processes rather than isolated prompt--response pairs; validity, developing uncertainty guarantees that remain meaningful und
As LLMs move from research to widespread deployment, ensuring their trustworthiness and reliability in real-world, dynamic environments becomes a critical and immediate challenge.
Statistical inference for LLM monitoring addresses fundamental issues of validity and reliability, which are crucial for the adoption of AI agents and complex AI systems in high-stakes applications.
The focus shifts from static evaluation of LLMs to dynamic, real-time monitoring and validation of their behavior, acknowledging their evolving contexts and interactions.
- · AI safety researchers
- · LLM developers
- · Enterprises deploying AI
- · Regulatory bodies
- · Companies with unreliable AI systems
- · Ad-hoc AI monitoring solutions
Improved methods for monitoring and ensuring the reliability of large language models in deployment.
Increased trust and accelerated adoption of LLM-powered applications across industries due to enhanced validity guarantees.
Formalized standards and regulatory frameworks for AI system validity emerge, potentially leading to 'AI compliance' as a new industry segment.
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Read at arXiv cs.LG