The LENS framework applied to 192 real-world settings shows moderate natural prompt distribution shifts cause 73% average performance loss in deployed LLMs, especially across user groups and regions.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , pages=
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KARITA integrates knowledge-driven augmentation and retrieval to improve classification performance under temporal shifts across clinical, legal, and scientific domains.
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Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
The LENS framework applied to 192 real-world settings shows moderate natural prompt distribution shifts cause 73% average performance loss in deployed LLMs, especially across user groups and regions.
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Knowledge-driven Augmentation and Retrieval for Integrative Temporal Adaptation
KARITA integrates knowledge-driven augmentation and retrieval to improve classification performance under temporal shifts across clinical, legal, and scientific domains.