MS MARCO Chameleons: Challenging the MS MARCO Leaderboard with Extremely Obstinate Queries
Bibliographic record
Abstract
During the recent years and with the growing influence of neural architectures, tasks such as ad hoc retrieval have witnessed an impressive improvement in performance. For instance, the performance of rankers on the passage retrieval task on the MS MARCO dataset has improved by an order of magnitude in less than two years. In this paper, we go beyond the overall performance of the state of the art rankers and empirically study their performance from a finer-grained perspective. We find that while neural rankers have been able to consistently improve performance, this has been in part thanks to a specific set of queries from within the larger query set. We systematically show that there are subsets of queries that are difficult for each and every one of the neural rankers, which we refer to as obstinate queries. We show the obstinate queries are similar to easier queries in terms of their number of available relevant judgement documents and the length of the query itself but they are extremely more difficult to satisfy by existing rankers. Furthermore, we observe that query reformulation methods cannot help these queries. On this basis, we present three datasets derived from the MS MARCO Dev set, called the MS MARCO Chameleon datasets. We believe that the next breakthrough in performance would need to necessarily consider the queries in the MS MARCO Chameleons, as such, propose that a well-rounded evaluation strategy for any new ranker would need to include performance measures on both the overall MS MARCO dataset as well as the proposed MS MARCO Chameleon datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".