Effective measures for inter-document similarity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While supervised learning-to-rank algorithms have largely supplanted unsupervised query-document similarity measures for search, the exploration of query-document measures by many researchers over many years produced insights that might be exploited in other domains. For example, the BM25 measure substantially and consistently outperforms cosine across many tested environments, and potentially provides retrieval effectiveness approaching that of the best learning-to-rank methods over equivalent features sets. Other measures based on language modeling and divergence from randomness can outperform BM25 in some circumstances. Despite this evidence, cosine remains the prevalent method for determining inter-document similarity for clustering and other applications. However, recent research demonstrates that BM25 terms weights can significantly improve clustering. In this work, we extend that result, presenting and evaluating novel inter-document similarity measures based on BM25, language modeling, and divergence from randomness. In our first experiment we analyze the accuracy of nearest neighborhoods when using our measures. In our second experiment, we analyze using clustering algorithms in conjunction with our measures. Our novel symmetric BM25 and language modeling similarity measures outperform alternative measures in both experiments. This outcome strongly recommends the adoption of these measures, replacing cosine similarity in future work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it