Beyond Semantics: Optimizing Surface Formatting for Robust Retrieval-Augmented Generation

ACL ARR 2026 January Submission4699 Authors

Keywords: Retrieval-Augmented Generation, Large Language Models, Context Modeling

Abstract: Retrieval-Augmented Generation (RAG) is essential for extending Large Language Models (LLMs) to knowledge-intensive tasks. While prior research has primarily focused on retrieval quality and prompting strategies, the influence of how the retrieved documents are framed, i.e., context format, remains underexplored. We demonstrate that semantically identical inputs can yield drastically different behaviors based solely on superficial formatting choices. Through mechanistic analysis, we reveal the underlying factors that govern performance differences, showing that suboptimal formats can disrupt information grounding. Moreover, we introduce Contextual Normalization, a lightweight framework that calibrates the input surface formats to the model’s internal dynamics. By optimizing the proposed metric, it adaptively selects the most effective format without requiring architectural changes. Extensive experiments demonstrate that the method consistently enhances robustness and accuracy, particularly in challenging long-context scenarios. These findings underscore that reliable RAG depends not only on retrieving the right content, but also on how that content is presented, offering both new empirical evidence and practical techniques.

Paper Type: Long

Research Area: Special Theme (conference specific)

Research Area Keywords: Interpretability/Explainability of LLMs, Retrieval-Augmented Generation

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 4699