Trie-Based Experiment Plans for Efficient IR Pipeline Experiments

Jul 16, 2026

Search engines often use cascading retrieval pipelines that combine multiple retrievers and iteratively refine rankings, making end-to-end evaluation difficult and motivating careful measurement of early-stage recall and later-stage precision. PyTerrier supports declarative pipeline construction and evaluation but can be costly when shared components are repeatedly executed. This work introduces trie-based experiment plans that reuse overlapping pipeline prefixes via a compact data structure, improving efficiency over linear execution. On an MSMARCO v2 study, experiment duration drops by 26%, with additional validation from a user study.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Introduction

Related Work

Experiment Plans with Trie Structure

Experimental Benefits

User Study Feedback

Conclusion

Trie-based Experiment Plans for Efficient IR Pipeline Experiments Irene Anu , Craig Macdonald University of Glasgow, United Kingdom Abstract Search engines are often formulated as cascading pipelines, where successive stages combine the results of different retrievers, and iteratively refine the ranking of candidate documents to obtain a final ranking, which can be presented to a user, or provided as context to an LLM. Such pipelines can be complex to evaluate in an end-to-end manner, necessitating measurement of Recall of early stages, and Precision of later stages, which are often interchangeable. PyTerrier is ideal for building and evaluating cascading retrieval pipelines, due to its declarative nature for pipeline construction and wide ecosystem of retrievers and rerankers. However, comparative evaluation of pipelines can be expensive due to repeated components. In this work, we describe the use of a triedata structure to formulate an experiment plan for comparative pipeline experiments that enhances experiment efficiency compared to a sequential “linear" plan. Empirically, on a demonstration experiment involving BM25, MonoT5 and DuoT5 on MSMARCO v2, we observe a 26% reduction in experiment duration. Finally, we report on a user study of undergraduate and postgraduate research students’ use of the experiment plans. Keywords Information Retrieval Experiments 1. Introduction The scale of corpora available to search is constantly growing which necessitates that modern Information Retrieval (IR) systems balance the accuracy of the retrieved results with the computational cost to produce them [1] . Retrieval systems are often formulated as pipelines, notably cascading ranking pipelines that progressively re-rank a candidate set of documents with more expensive retrieval models [2, 3, 4] . In such pipelines, a dense retriever is used to obtain an initial candidate set which are further refined by neural rerankers such as mono, duo [5] or even listwise rerankers [6] . With the prevalence of such pipeline architectures, PyTerrier allows composition of pipelines ina declarative manner with a suite of function objects collectively called transformers. In this Python formulation, operators are used to indicate combinations of Transformers – for instance, ≫ and % are used to indicate composition of Transformers and rank cutoff operations respectively. This allows the rapid formulation of pipeline variants with alternative retrieval backends, ranking or reranking models, query reformulators, etc. -indeed there exists a wide ecosystem of plugins addressing functionality including dense retrieval [7, 8], rerankers, and RAG [9] . Such pipelines can also be easily visualised [10](see Figure 1), or evaluated using test collections obtained from ir_datasets [11](a lightweight library that provides easy access to a variety of IR corpora and query sets) . Indeed, it is common for researchers to construct and compare multiple pipeline variants to evaluate the effectiveness of new techniques. However, a practical challenge that arises is the need to re-run large shared components of these pipelines, resulting in substantial redundant computation. For example, Penha et al. [12] conducts multiple experiments that all rely on the same first-stage retriever BM25, leading to repeated execution of identical retrieval steps. This inefficiency emphasises the need for more efficient IR experimentation methods. In this work, we propose the notion of experiment plans – the decomposition of a set of pipeline comparisons involving shared components into a minimal set of executions. For instance, a linear plan ReNeuIR’26: Workshop on Reaching Efficiency in Neural Information Retrieval, July 24, 2026, Melbourne, Australia $ [2772146I@student.gla.ac.uk](2772146I@student.gla.ac.uk) (I. Anu); [craig.macdonald@glasgow.ac.uk](craig.macdonald@glasgow.ac.uk) (C. Macdonald) 􀀚 0009-0003-0157-3812 (I. Anu); 0000-0003-3143-279X (C. Macdonald)

FAQs

What problem does the paper address in IR pipeline experimentation?

Evaluating many related cascading pipeline variants can be expensive because shared components are repeatedly re-executed, causing redundant computation.

How do trie-based experiment plans improve efficiency?

They build an experiment plan that reuses all overlapping pipeline prefixes by representing shared stages with a trie/radix-tree structure, reducing repeated executions compared to a linear plan.

What results and validation does the paper report?

On a demonstrated experiment using BM25, MonoT5, and DuoT5 on MSMARCO v2, the method reduces experiment duration by 26%, and the approach is further assessed through a user study with research students.

Jul 16, 2026

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