Bibliographic record
Abstract
Many current test collections require the use of expert judgments during construction. The true label of each document is given by an expert assessor. However, the cost and effort associated with expert training and judging are typically quite high in the event where we have a high number of documents to judge. One way to address this issue is to have each document judged by multiple non-expert assessors at a lower expense. However, there are two key factors that can make this method difficult: the variability across assessors' judging abilities, and the aggregation of the noisy labels into one single consensus label. Much previous work has shown how to utilize this method to replace expert labels in the relevance evaluation. However, the effects of relevance judgment errors on the ranking system evaluation have been less explored. \n \nThis thesis mainly investigates how to best evaluate information retrieval systems with noisy labels, where no ground-truth labels are provided, and where each document may receive multiple noisy labels. Based on our simulation results on two datasets, we find that conservative assessors that tend to label incoming documents as non-relevant are preferable. And there are two important factors affect the overall conservativeness of the consensus labels: the assessor's conservativeness and the relevance standard. This important observation essentially provides a guideline on what kind of consensus algorithms or assessors are needed in order to preserve the high correlation with expert labels in ranking system evaluation. Also, we systematically investigate how to find the consensus labels for those documents with equal confidence to be either relevant or non-relevant. We investigate a content-based consensus algorithm which links the noisy labels with document content. We compare it against the state-of-art consensus algorithms, and find that, depending on the document collection, this content-based approach may help or hurt the performance.
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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 | 0.038 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".