The paper proposes a method for automatically generating keywords for virtual datasets in a data virtualization service (DVS) using large language models (LLMs). Five strategies for selecting text inputs to the LLMs were evaluated across five application case studies, with experiments involving three LLM generators and two embedding techniques. Results indicate that the effectiveness of each strategy depends on the quality of the text descriptions, leading to a multi‑strategy keyword generation service (KGS) integrated into the DVS.
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
arXiv:2605. 19276v3 Announce Type: replace-cross Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large language models (LLMs).
arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model b...
arXiv:2604. 10311v2 Announce Type: replace Abstract: Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications.
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
arXiv:2607. 06482v1 Announce Type: cross Abstract: Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings.
arXiv:2609.20886v1 Announce Type: cross Abstract: Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tab...
The paper introduces EXYGEN, a framework that enables conversational access to large knowledge graphs by combining VoID descriptions, ShEx schemas, retrieved triples, and example question‑query pairs in a retrieval‑augmented generation pipeline. On the SciQA benchmark, this approach achieves an exact‑match score of 0.419 without fine‑tuning any large language model, and shows that larger general‑purpose LLMs can outperform smaller code‑specialized ones when provided sufficient context. To scale metadata generation for very large KGs, the authors propose a predicate‑coverage‑aware parallel graph sampling strategy that preserves structural diversity, reduces runtime by over 80× on OpenCitations Meta and GESIS, and is the only tractable method for obtaining complete metadata on ORKG.
arXiv:2604. 18543v4 Announce Type: replace Abstract: Constructing environments for training and evaluating claw-like agents remains a manual, human-intensive process that does not scale.
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
The paper presents a framework that combines human-curated vocabularies with a few human-written exemplars to enable large language models (LLMs) to generate coherent text in extremely low-resource or unseen languages. By sampling candidate words from a Zipfian distribution and providing syntactic signals, the method achieves fluent sentence-level generation and promising paragraph-level results across languages with varying resource levels. The study evaluates output quality using lexical diversity, off-target ratio, and human fluency judgments, demonstrating that reasoning-enabled LLMs can effectively bootstrap synthetic data even when digital resources are scarce.
arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).