Noisy Perturbations for Estimating Query Difficulty in Dense Retrievers
Bibliographic record
Abstract
Estimating query difficulty, also known as Query Performance Prediction (QPP), is concerned with assessing the retrieval quality of a ranking method for an input query. Most traditional unsupervised frequency-based models and many recent supervised neural methods have been designed specifically for predicting the performance of sparse retrievers such as BM25. In this paper we propose an unsupervised QPP method for dense neural retrievers which operates by redefining the well-known concept of query robustness i.e., a more robust query to perturbations is an easier query to handle. We propose to generate query perturbations for measuring query robustness by systematically injecting noise into the contextualized neural representation of each query. We then compare the retrieved list for the original query with that of the perturbed query as a way to measure query robustness. Our experiments on four different query sets including MS MARCO, TREC Deep Learning track 2019 and 2020 and TREC DL-Hard show consistently improved performance on linear and ranking correlation metrics over the state of the art.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".