Multiple Linear Combination Approaches for Information Search in Ranking
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
Since the well-known BM25 [1] was proposed, BM25 and its enhanced version [2] – [4] have dominated the document/passage ranking task for a long time. However, with the advent of deep learning models like BERT [5] , these pre-trained models have achieved noticeable progress in various information retrieval (IR) tasks. But, as BM25 is a "bag of words" retrieval method by matching keywords, it remains a better option for passage ranking in some exceptional cases, like identifying names [6] . Therefore, fusing BM25 with deep learning models is a natural idea to benefit the ranking results. This paper discusses various linear methods of combing BM25 with BERT to see how they affect the final results of the models. We conduct experiments on the MS MARCO V2 dataset, which show convincing results.
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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 | 0.001 | 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 | 0.002 |
| 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