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arXiv · 2606.17721

Understanding and Debugging Failures in N-Gram-Based Generative Retrieval

Richard Takacs·Adrian Bracher·Svitlana Vakulenko
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Abstract

Generative Retrieval (GR) is an emerging Information Retrieval (IR) paradigm that is motivated by increasingly capable language models. In GR, a model directly generates identifiers for relevant documents. While these systems offer unique advantages, they also introduce distinct failure mechanisms. We explore these failure modes in three contributions: (1) We present a taxonomy of GR failure modes based on GR literature. (2) We empirically investigate failure in a subset of GR: ngram-based methods, more specifically, SEAL and MINDER. Our analysis reveals common issues, such as ambiguous docids, low identifier diversity, and the disproportionate impact of specific identifiers. (3) We introduce a new web-based tool that helps the IR community analyze generated ngrams and their respective contribution to the final ranking, providing an intuitive interface to identify where such GR methods go wrong.

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Richard Takacs, Adrian Bracher, Svitlana Vakulenko. 2026-06-16. Understanding and Debugging Failures in N-Gram-Based Generative Retrieval. https://arxiv.org/abs/2606.17721

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