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Wikipedia editors plot strike and banner sabotage after Wikimedia layoffs
The Wikimedia Foundation (WMF) has sparked a revolt among Wikipedia editors after disbanding the engineering team responsible for many community-requested fixes and moderation tools. The Register was tipped off this week to growing unrest inside the Wikipedia editing community following the WMF's decision to disband its Community Tech team, the group responsible for triaging and developing editor-requested bug fixes, moderation tools, and workflow improvements through the long-running...
WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia
arXiv:2507.03373v2 Announce Type: replace Abstract: Given Wikipedia's role as a trusted source of high-quality, reliable content, concerns are growing about the proliferation of low-quality machine-generated text (MGT) produced by large language models (LLMs) on its platform. Reliable detection of MGT is therefore essential.
TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices
arXiv:2605.31113v1 Announce Type: new Abstract: Automatically detecting machine-generated text (MGT) is critical to maintaining the knowledge integrity of user-generated content (UGC) platforms such as Wikipedia. Existing detection benchmarks primarily focus on \textit{generic} text generation tasks (e.g., ``Write an article about machine learning.''). However, editors frequently employ LLMs for specific writing tasks (e.g., summarisation).
Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages
arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, this task instantiates as Citation Needed Detection (CND), which flags claims lacking supporting citations. However, existing research has largely overlooked lower-resource languages, and recent AFC pipelines rely on large language models (LLMs), which are inaccessible to low-resource organizations.
Hundreds of Wikipedia editors are threatening to go on strike and the reason is this
Here's a summary of the article: Hundreds of Wikipedia editors are threatening a potential strike after the Wikimedia Foundation disbanded its Community Tech team. Volunteers fear this move will leave community-requested tools and technical support neglected, potentially impacting the platform's daily operations and content maintenance.
HalleluBERT: Let Every Token That Has Meaning Bear Its Weight
arXiv:2510.21372v2 Announce Type: replace Abstract: Transformer-based models have advanced NLP, yet Hebrew still lacks a RoBERTa encoder that is trained at scale and released in both base and large variants. We present HalleluBERT, a RoBERTa-based encoder family trained from scratch on 49.1~GB of deduplicated Hebrew web text and Wikipedia using a Hebrew-specific byte-level BPE vocabulary. On native Hebrew benchmarks for named entity recognition (BMC, NEMO) and sentiment classification...
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
Announce Type: replace Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematically diagnose this "modality gap" by evaluating seven MLLMs across seven benchmarks in five input modes, spanning both synthetically rendered text and realistic document images from arXiv PDFs to Wikipedia pages. We find that the gap is highly sensitive to rendering choices such as...
LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?
Announce Type: replace Abstract: We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, models must efficiently navigate Wikipedia hyperlinks step by step to reach a target page from a given source, requiring look-ahead planning and the ability to reason about how concepts are connected in the real world. We evaluate a broad set of open- and closed-source models, including Gemini-3, GPT-5, and Claude...