LLM + Vector Data: Coupling of Large Language Models with Vector Data Management for Enhancing Data Science
Bibliographic record
Abstract
The emergence of generative AI (GenAI) is a major driving force behind the modern data science ecosystem, a field that exploits data as the central asset for actionable insights. Analogously, GenAI is a form of artificial intelligence which learns from massive datasets to generate new data, showcasing human-like creativity in text, images to code, speech, and video. Two critical pillars of the GenAI technology are large language models (LLMs) and vector data. In particular, LLMs are a category of genAI models that emphasize on generating new text contents. On the other hand, there is also an upsurge of dense, high-dimensional, billion-scale vector data from deep learning models that embed complex data, e.g., text, multimedia, graphs, and tables into vector representations aiming to preserve semantic similarity. Since LLMs operate on vector data at various stages consisting of pre-training, fine-tuning, inference, and retrieval-augmented generation (RAG), coupling large language models with vector data management is essential for enhancing data science services with cross-modal data querying and generation. It creates new opportunities and challenges in areas such as accuracy, consistency, efficiency, scalability, privacy, fairness, explainability, data regulations, software-hardware collaboration, and cloud-native systems. The workshop aims to advance the understanding of how LLMs and vector data management can cooperatively contribute to data science solutions.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".