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arXiv · 2606.11961

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

Antonio Pelusi·Stefano Braghin·Alberto Trombetta
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Abstract

Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new distributions without parameter updates. We investigate the limits of ICL for structured generation under distribution mismatch, using high-cardinality tabular data as a controlled test case, and identify a structural failure mode we term \textit{categorical prior lock-in}: the inability of ICL to update the model's prior over token distributions inherited from pre-training. Across two 7B-parameter open-weight models, ICL improves numerical fidelity with additional examples but exhibits a sharp ceiling on categorical distributions, failing to reproduce rare classes entirely. Parameter-efficient fine-tuning (LoRA) overcomes these limitations but introduces measurable memorization risk and, in some cases, destabilizes structured output generation, highlighting a fundamental trade-off between adaptability and privacy.

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Antonio Pelusi, Stefano Braghin, Alberto Trombetta. 2026-06-10. Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data. https://arxiv.org/abs/2606.11961

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