Document Expansion Baselines and Learned Sparse Lexical Representations for MS MARCO V1 and V2
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
With doc2query, we train a neural sequence-to-sequence model that, given an input span of text, predicts a natural language query that the text might answer. These predictions can be viewed as document expansions that feed standard bag-of-words term weighting models such as BM25 or neural retrieval models based on learned sparse lexical representations such as uniCOIL. Previous experiments on the MS MARCO datasets have demonstrated the effectiveness of these methods, and they serve as baselines that are widely used by the community today. Following the recent release of the MS MARCO V2 passage and document ranking test collections, we have refreshed our doc2query and uniCOIL models. This work describes a number of resources that support competitive, reproducible baselines for both the MS MARCO V1 and V2 test collections using our Anserini and Pyserini IR toolkits. Together, they provide a solid foundation for future research on neural retrieval models using the MS MARCO datasets and beyond.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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