SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics
arXiv:2605.31575v1 Announce Type: cross Abstract: Scalable information retrieval testing needs corpora that are large enough to stress index construction, ranking latency, query routing, and evaluation tooling, yet human-judged test collections remain expensive and may be unavailable when documents are private or still under design. This paper introduces SPECTRA, a reproducible framework for generating synthetic text corpora and retrieval test collections through a separation of latent topical structure, surface text realization, metadata controls, query intent generation, and deterministic re
The increasing complexity and scale of AI models necessitate more robust and scalable evaluation methodologies, driving the development of synthetic test environments.
This development addresses a critical bottleneck in AI research and development: the prohibitive cost and limitations of human-judged test sets for information retrieval.
The ability to generate large-scale, controlled, and reproducible test collections will accelerate progress in information retrieval, especially for proprietary or rapidly evolving domains.
- · AI/ML researchers
- · Information retrieval companies
- · Companies with proprietary data
- · AI evaluation platforms
- · Developers relying solely on limited human-judged datasets
- · Consultancies specializing in bespoke, small-scale IR evaluations
Faster iteration and improvement cycles for information retrieval models become possible.
Reduced barriers to entry for developing and evaluating IR systems, democratizing access to training and testing data.
Enhanced competition and more rapid innovation in search, recommendation, and AI agent capabilities due to superior evaluation tools.
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Read at arXiv cs.AI