Using controlled query generation to evaluate blind relevance feedback algorithms
Bibliographic record
Abstract
Currently in document retrieval there are many algorithms each with different strengths and weakness. There is some difficulty, however, in evaluating the impact of the test query set on retrieval results. The traditional evaluation process, the Cranfield evaluation paradigm, which uses a corpus and a set of user queries, focuses on making the queries as re-alistic as possible. Unfortunately such query sets lack the fine grained control necessary to test algorithm properties. We present an approach called Controlled Query Generation (CQG) that creates query sets from documents in the corpus in a way that regulates the theoretic information quality of each query. This allows us to generate reproducible and well defined sets of queries of varying length and term specificity. Imposing this level of control over the query sets used for testing retrieval algorithms enables the rigorous simulation of different query environments to identify specific algorithm properties before introducing user queries. In this work, we demonstrate the usefulness of CQG by generating three dif-ferent query environments to investigate characteristics of two blind relevance feedback approaches.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".