arXiv:2607.03528v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as critical decision-making components in high-stakes real-world AI systems, rendering LLM reliability a foremost practical concern. In this paper, we focus on enhancing LLM reliability through selective prediction (SP), a strategy that allows an LLM to only predict for inputs where it is likely to be correct (i.e., coverage) and hence reduce the error rate (i.e., risk) on that portion of inputs -- flagging the remaining inputs for future human discretion. In other words, SP improves LLM re
As LLMs are increasingly integrated into critical real-world systems, the urgency to address their reliability and error rates becomes paramount for practical deployment.
Improving LLM reliability through selective prediction enables safer and more trustworthy AI deployments in high-stakes environments, reducing human oversight burden while mitigating risks.
LLMs can now be deployed with higher confidence in sensitive applications, as they flag uncertain outputs for human review rather than making potentially erroneous predictions.
- · AI system developers
- · High-stakes industries (e.g., healthcare, finance)
- · AI safety researchers
- · Users of critical AI applications
- · Developers of unreliable LLMs
- · AI applications lacking error handling
- · Manual review processes for all AI outputs
Increased adoption of LLMs in regulated and safety-critical sectors due to enhanced reliability.
Demand for specialized human-in-the-loop interfaces and workflows to manage flagged AI predictions.
The development of 'AI-assisted human discretion' as a new category of professional services, blurring lines between AI and human decision-making workflows.
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Read at arXiv cs.AI