A new paper introduces the concept of "inference-time model shift" to describe how changes in language model inference configurations can affect conformal prediction (CP) performance. The researchers argue that standard CP evaluation protocols, which assume consistent configurations between calibration and deployment, are insufficient. Their analysis across eight language models and multiple benchmarks reveals that matched evaluation can overestimate deployment-time coverage, with the impact varying by model and dataset. The study advocates for reporting inference configurations as a critical factor in CP evaluations for language model systems. AI
IMPACT Highlights a critical flaw in evaluating language model reliability, potentially impacting how safety and uncertainty are measured in deployed systems.
RANK_REASON Academic paper on a novel concept for evaluating language model performance. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Conformal prediction
- Hugging Face
- Inference-Time Model Shift
- Language Models
- split-conformal exchangeability
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