Efficient Document-at-a-Time and Score-at-a-Time Query Evaluation for Learned Sparse Representations
Bibliographic record
Abstract
Researchers have had much recent success with ranking models based on so-called learned sparse representations generated by transformers. One crucial advantage of this approach is that such models can exploit inverted indexes for top- k retrieval, thereby leveraging decades of work on efficient query evaluation. Yet, there remain many open questions about how these learned representations fit within the existing literature, which our work aims to tackle using four representative learned sparse models. We find that impact weights generated by transformers appear to greatly reduce opportunities for skipping and early exiting optimizations in well-studied document-at-a-time ( DaaT ) approaches. Similarly, “off-the-shelf” application of score-at-a-time ( SaaT ) processing exhibits a mismatch between these weights and assumptions behind accumulator management strategies. Building on these observations, we present solutions to address deficiencies with both DaaT and SaaT approaches, yielding substantial speedups in query evaluation. Our detailed empirical analysis demonstrates that both methods lie on the effectiveness–efficiency Pareto frontier, indicating that the optimal choice for deployment depends on operational constraints.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".