DRUM: Learning Demonstration Retriever for Large MUlti-modal Models
Bibliographic record
Abstract
Recently, large language models (LLMs) have demonstrated impressive capabilities in dealing with new tasks with the help of in-context learning (ICL).In the study of Large Vision-Language Models (LVLMs), when implementing ICL, researchers usually adopt the naive strategies like fixed demonstrations across different samples, or selecting demonstrations directly via a visual-language embedding model.These methods do not guarantee the configured demonstrations fit the need of the LVLMs.To address this issue, we propose a novel framework, demonstration retriever for large multimodal model (DRUM), which fine-tunes the CLIP embedding model to better meet the LVLM's needs.First, we discuss the retrieval strategies for a visual-language task, assuming an embedding model is given.And we propose to concate the image and text embeddings to enhance the retrieval performance.Second, we propose to re-rank the the embedding model's retrieved demonstrations via the LVLM's feedbacks, and calculate a list-wise ranking loss for training the embedding model.Third, we propose an iterative demonstration mining strategy to improve the training of the embedding model.Through extensive experiments on 3 types of visual-language tasks, 7 benchmark datasets, our DRUM framework is proven to be effective in boosting the LVLM's in-context learning performance via retrieving more proper demonstrations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".