arXiv:2606.11961v1 Announce Type: new 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
This paper offers a foundational insight into current limitations of LLMs for structured data precisely when enterprises are aggressively exploring their application in such domains.
Understanding the 'categorical prior lock-in' directly impacts the strategic deployment and architectural choices for enterprise AI, particularly in data-intensive sectors, highlighting a critical barrier to current generative AI capabilities.
The perceived generality of in-context learning for structured data is diminished, forcing a re-evaluation of LLM architectures and prompting strategies for robust, conditional generation in non-textual formats.
- · Specialized AI models
- · Hybrid AI architectures
- · Data engineering firms
- · R&D in new prompt engineering
- · LLMs for generic structured data tasks
- · Uncritical ICL adoption
- · Companies relying solely on off-the-shelf LLMs
Companies will re-evaluate and likely reduce reliance on pure in-context learning for critical structured data generation tasks.
There will be increased investment in fine-tuning, specialized models, or hybrid architectures combining LLMs with traditional methods to generate structured data reliably.
This could lead to a bifurcation of the AI market, with generalist LLMs dominating unstructured text, while specialized, perhaps smaller, models or new paradigms become essential for structured data applications.
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Read at arXiv cs.LG