TREC-8 Experiments at Maryland: CLIR, QA and Routing
Bibliographic record
Abstract
The University of Maryland team participated in four aspects of TREC-8: the ad hoc retrieval task, the main task in the cross-language retrieval (CLIR) track, the question answering track, and the routing task in the filtering track. The CLIR method was based on Pirkola's method for Dictionary-based Query Translation, using freely available dictionaries. Broad-coverage parsing and rule-based matching was used for question answering. Routing was performed using Latent Semantic Indexing in profile space. This working notes paper presents preliminary results. 1 Introduction The Eighth Text REtrieval Conference (TREC-8) offered many more attractive evaluation opportunities that our team could have pursued, so we chose to participate in four aspects of the work that are aligned particularly closely with our ongoing work. In Cross-Language Information Retrieval track (CLIR), we focused on rapid retargetability, seeking to learn how well we could do with freely available resources that...
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".