CERTEM
Bibliographic record
Abstract
Entity resolution (ER) aims at identifying record pairs that refer to the same real-world entity. Recent works have focused on deep learning (DL) techniques, to solve this problem. While such works have brought tremendous enhancements in terms of effectiveness in solving the ER problem, understanding their matching predictions is still a challenge, because of the intrinsic opaqueness of DL based solutions. Interpreting and trusting the predictions made by ER systems is crucial for humans in order to employ such methods in decision making pipelines. We demonstrate CERTEM an explanation system for ER based on CERTA, a recently introduced explainability framework for ER, that is able to provide both saliency explanations, which associate each attribute with a saliency score, and counterfactual explanations, which provide examples of values that can flip a prediction. In this demonstration we will showcase how CERTEM can be effectively employed to better understand and debug the behavior of state-of-the-art DL based ER systems on data from publicly available ER benchmarks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".