RISA: Response Inspection and Selective Actions for Refusal Calibration in Large Language Models
RISA framework calibrates LLM refusal behaviour to reduce both harmful outputs and false refusals — a balancing act critical for enterprises deploying AI under EU AI Act obligations.
Summary written by editorial AI · Source link below
arXiv:2609.00790v1 Announce Type: new Abstract: Reliable refusal behavior requires Large Language Models (LLMs) to reject harmful prompts with only answering benign ones. Incorrect refusal behavior can either expose users to harmful responses or prevent users from obtaining useful answers. Training-time alignment improves refusal behavior by updating model parameters with safety data, but requires additional computation and training. In contrast, inference-time alignment aims to modify LLM beha
Editorial Analysis
Overly aggressive refusal degrades business utility while too-permissive models create liability; calibration techniques like RISA help enterprises hit the EU AI Act's required balance.
Integrate refusal-calibration testing into your LLM evaluation pipeline to measure and document both harmful-output rates and false-refusal rates.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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