For full technical details + compliance Datasheet see our preprint @ arxiv.org/abs/2510.13996

As for German-specific models trained on this data... stay tuned 👀

The data spans 7 text domains:
🌐 Web: Wikipedia, GitHub, social media
💬 Political: Parliamentary proceedings, speeches
⚖️ Legal: Court decisions, federal & EU law
📰 News: Newspaper archives
🏦 Economics: public tenders
📚 Cultural: Digital heritage collections
🔬 Scientific: Papers, books, journals

This means:
✅ Every document has verifiable usage rights (min. CC-BY-SA 4.0 and allows commercial use)
✅ Full institutional provenance for reduced compliance risks
✅ Systematic PII removal + quality filtering, ready for training
✅ Rich metadata for downstream customization

The current problem: training data is primarily sourced from Web crawls, which give you scale but unclear licensing. This blocks models from commercial deployment and research. We took a different path: systematically collecting German text from 41 institutional sources with explicit open licenses.

Congratulations to the authors @heinrich.merker.id, @maik-froebe.bsky.social, @benno-stein.de, @martin-potthast.com, @matthias-hagen.bsky.social from @uni-jena.de, Uni Weimar, @unikassel.bsky.social, @hessianai.bsky.social, @scadsai.bsky.social!

Honored to win the ICTIR Best Paper Honorable Mention Award for "Axioms for Retrieval-Augmented Generation"!
Our new axioms are integrated with ir_axioms: github.com/webis-de/ir_...
Nice to see axiomatic IR gaining momentum.

Congrats to the authors @lgnp.bsky.social @timhagen.bsky.social @maik-froebe.bsky.social @matthias-hagen.bsky.social @benno-stein.de @martin-potthast.com @hscells.bsky.social from @unikassel.bsky.social @hessianai.bsky.social @scadsai.bsky.social @unituebingen.bsky.social @uni-jena.de & Uni Weimar

Congratulations to the authors @lgnp.bsky.social @deckersniklas.bsky.social @martin-potthast.com @hscells.bsky.social !

📄 Preprint: arxiv.org/abs/2407.21515
💻 Code: github.com/webis-de/ada...

Results on BEIR demonstrate that our method matches teacher distillation effectiveness, while using only 13.5% of the data and achieving 3-15x training speedup. This makes effective bi-encoder training more accessible, especially for low-resource settings.

The key idea: we can use the similarity predicted by the encoder itself between positive and negative documents to scale a traditional margin loss. This performs implicit hard negative mining and is hyperparameter-free.

…human texts today, contextualize the findings in terms of our theoretical contribution, and use them to make an assessment of the quality and adequacy of existing LLM detection benchmarks, which tend to be constructed with authorship attribution in mind, rather than authorship verification. 3/3

…limits of the field. We argue that as LLMs improve, detection will not necessarily become impossible, but it will be limited by the capabilities and theoretical boundaries of the field of authorship verification.

We conduct a series of exploratory analyses to show how LLM texts differ from… 2/3

🧵 4/4 The shared task continues the research on LLM-based advertising. Participants can submit systems for two sub-tasks: First, generate responses with and without ads. Second, classify whether a response contains an ad.
Submissions are open until May 10th and we look forward to your contributions.

🧵 3/4 In a lot of cases, survey participants did not notice brand or product placements in the responses. As a first step towards ad-blockers for LLMs, we created a dataset of responses with and without ads and trained classifiers on the task of identifying the ads.
dl.acm.org/doi/10.1145/...

🧵 2/4 Given the high operating costs of LLMs, they require a business model to sustain them and advertising is a natural candidate.
Hence, we have analyzed how well LLMs can blend product placements with "organic" responses and whether users are able to identify the ads.
dl.acm.org/doi/10.1145/...

🧵 4/4 Credit and thanks to the author team @lgnp.bsky.social @timhagen.bsky.social @maik-froebe.bsky.social @matthias-hagen.bsky.social @benno-stein.de @martin-potthast.com @hscells.bsky.social – you can also catch some of them at #ECIR2025 currently if you want to chat about RAG!