A Framework for Evaluation of Machine Reading Comprehension Gold\n Standards
Bibliographic record
Abstract
Machine Reading Comprehension (MRC) is the task of answering a question over\na paragraph of text. While neural MRC systems gain popularity and achieve\nnoticeable performance, issues are being raised with the methodology used to\nestablish their performance, particularly concerning the data design of gold\nstandards that are used to evaluate them. There is but a limited understanding\nof the challenges present in this data, which makes it hard to draw comparisons\nand formulate reliable hypotheses. As a first step towards alleviating the\nproblem, this paper proposes a unifying framework to systematically investigate\nthe present linguistic features, required reasoning and background knowledge\nand factual correctness on one hand, and the presence of lexical cues as a\nlower bound for the requirement of understanding on the other hand. We propose\na qualitative annotation schema for the first and a set of approximative\nmetrics for the latter. In a first application of the framework, we analyse\nmodern MRC gold standards and present our findings: the absence of features\nthat contribute towards lexical ambiguity, the varying factual correctness of\nthe expected answers and the presence of lexical cues, all of which potentially\nlower the reading comprehension complexity and quality of the evaluation data.\n
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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 | 0.074 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".