Gosling Grows Up: Retrieval with Learned Dense and Sparse Representations Using Anserini
Bibliographic record
Abstract
The Anserini IR toolkit has come a long way since efforts began in 2015. Although the goals of the project - to bridge research and practice in information retrieval, and to provide reproducible, easy-to-use baselines - have remained constant, the world has changed quite a bit. We discuss how Anserini has evolved in response to this changing environment, the most significant of which is the advent of transformer-based retrieval models that did not exist when the project started. The bi-encoder architecture provides a framework for understanding retrieval models based on dense and sparse vector representations, and offers a reference for conveying the capabilities of our toolkit. Anserini provides end-to-end first-stage retrieval based on single-vector learned dense and sparse representations, directly building on the open-source Lucene search library and the ONNX runtime. This minimal design accelerates the pace of research and fosters reproducibility, enabling ''two-click reproductions''. By better aligning research and practice, we increase the potential real-world impact of research innovations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".