6 papers
HalluHard: A Hard Multi-Turn Hallucination Benchmark
Dongyang Fan, Sebastien Delsad, Nicolas Flammarion +1
Large language models (LLMs) still produce plausible-sounding but ungrounded factual claims, a problem that worsens in multi-turn dialogue as context grows and early errors cascade…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
TiMoE: Time-Aware Mixture of Language Experts
Robin Faro, Dongyang Fan, Tamar Alphaidze +1
Large language models (LLMs) are typically trained on fixed snapshots of the web, which means that their knowledge becomes stale and their predictions risk temporal leakage: relyin…
URLs Help, Topics Guide: Understanding Metadata Utility in LLM Training
Dongyang Fan, Vinko Sabolčec, Martin Jaggi
Large Language Models (LLMs) are commonly pretrained on vast corpora of text without utilizing contextual metadata such as source, quality, or topic, leading to a context-free lear…
Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling Opt-Outs
Dongyang Fan, Vinko Sabolčec, Matin Ansaripour +4
The increasing adoption of web crawling opt-outs by copyright holders of online content raises critical questions about the impact of data compliance on large language model (LLM)…
Do Data Valuations Make Good Data Prices?
Dongyang Fan, Tyler J. Rotello, Sai Praneeth Karimireddy
As large language models increasingly rely on external data sources, compensating data contributors has become a central concern. But how should these payments be devised? We revis…