Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
arXiv:2606.30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query. Embedding a record with a text encoder first serializes its fields into a string, which forces a choice of field order. We show this choice, usually treated as an implementation detail, silently controls retrieval quality once the encoder is fine-tuned. A standard fine-tune loses 7.4 nDCG@10 points when the index is rebuilt under a different field order, because it reads absolute position instead of the fiel
This paper addresses a fundamental challenge in current AI model fine-tuning for structured data, specifically concerning the impact of field order that has likely been overlooked in many practical implementations.
This research highlights a significant vulnerability and optimization opportunity in how large language models process and retrieve structured metadata, directly impacting the efficiency and accuracy of AI applications.
AI models fine-tuned on structured data may need re-training or architectural adjustments to become truly permutation-invariant, preventing significant degradation in retrieval quality due to arbitrary serialization choices.
- · AI researchers in NLP and information retrieval
- · Companies building AI search and retrieval systems
- · Developers of structured data processing pipelines
- · AI systems relying on naive serialization of structured data
- · Organizations with heavily fine-tuned models sensitive to input order
- · Developers not aware of permutation invariance issues
Improved robustness and accuracy of AI models dealing with structured information retrieval.
Development of new architectural patterns or fine-tuning techniques for permutation-invariant embeddings, becoming a standard in data science.
Increased focus on data representation and serialization best practices across AI development, potentially leading to new data standards for AI input.
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Read at arXiv cs.LG