Colflor: Towards Bert-Size Vision-Language Document Retrieval Models
Bibliographic record
Abstract
Traditional document retrieval systems for PDFs, charts, and infographics rely heavily on Optical Character Recognition (OCR) pipelines to extract textual content, a process that is both error-prone and resource-intensive. Recent advancements in multimodal models like ColPali have enabled OCRfree retrieval by processing documents directly as images, but their large size (three billion parameters) makes them computationally expensive and impractical for large-scale applications. To address this limitation, we introduce ColFlor, an efficient OCR-free visual document retrieval model with only 174 million parameters. ColFlor achieves comparable performance to ColPali on text-rich English documents, with only a 1.8% decrease in performance (measured by NDCG@5 metric), while being significantly faster in image encoding (5.25 times faster) and query encoding (9.8 times faster). This makes OCR-free document retrieval systems more cost-effective for large-scale applications and more accessible to users with limited computational resources.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".