QQ is a language metadata toolkit designed for multilingual NLP research. It aggregates diverse language metadata into a graph of varieties, scripts, regions, identifiers, names, and relations, and offers access via a Python API, CLI, and browser explorer. The toolkit enables normalization of identifiers, metadata retrieval, relation traversal, and discovery of external resources containing a language, and is demonstrated through audits of the HuggingFace Hub, linking resources with different identifier systems, and generating reproducible language-reporting tables.
arXiv:2609.22494v1 Announce Type: new Abstract: In recent years, there has been a surge of interest in Cultural NLP, with substantial efforts to create globally inclusive NLP systems. The rapid growt...
The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to produce a fully machine-actionable graph in which ev...
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.
Institutional Books – Enriched Text is a 2025 release that transforms Harvard Library’s 983,004-volume collection (IB‑HL) into a multilingual, annotated dataset. The pipeline normalizes OCR text while preserving metadata, separating endmatter, detecting paragraph language, clustering duplicates, and scoring bits‑per‑byte, all wrapped in HTML‑like annotations. The resulting IB‑HL‑ET contains 217 B tokens across 983,003 volumes and 1.39 B annotated subtopic paragraphs, enabling users to customize output rather than accept a single editorial decision.
arXiv:2601.02933v4 Announce Type: replace Abstract: Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is n...
The paper introduces an end‑to‑end pipeline that harvests, validates, and models links between scholarly articles and their source code from journals such as JOSS, SoftwareX, and IPOL, as well as SIGMOD ARI reproducibility reports. It produces a curated set of 4,397 DOI‑repository pairs and defines two Wikidata‑based application profiles—one for articles and one for software—aligned with schema.org and CodeMeta. Using these profiles, the authors created 4,182 new Wikidata software items linked to their papers, while only 82 repositories were previously represented, and they demonstrate compatibility with the COAR Notify protocol for future live enrichment.
arXiv:2608.04186v3 Announce Type: replace Abstract: This paper presents a conceptual framework for developing an electronic explanatory dictionary of the Tajik language using large language models (L...
The paper introduces the first reproducible taxonomy for programming languages based on resource availability, categorizing 646 languages into four tiers. It finds that a small fraction (1.9%) of high-resource languages (Tier 3) generate the majority (74.6%) of tokens in major corpora, while the majority of languages (71.7%) are scarce and contribute only 1.0% of tokens. Statistical analysis confirms the extreme and systematic imbalance across tiers.
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:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
arXiv:2608.23263v1 Announce Type: new Abstract: The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to pro...
arXiv:2608.30107v1 Announce Type: cross Abstract: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and i...