activity
20172026
most citedAdapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

8 citations · 16 across the 48 of their papers we have counts for

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Showing 2026Show all

19 papers · 1 filter

cs.CL2026

Limitations of Automated Simulatability: LLM Simulators Can Bypass Explanations

Antonin Poché, Fanny Jourdan, Nils Feldhus +6

Simulatability is an evaluation protocol for explanations that quantifies their usefulness by how well they help a user predict a task model's outputs. Since human evaluation is co…

cs.CL2026

Compositional Multilingual and Behavioral Attribute Steering

Hyun Gu Kang, Daniil Gurgurov, Tanja Baeumel +2

This study examines the compositionality of steering vectors for language and behavioral control in large language models. Focusing on language, jailbreak, and conciseness, we inve…

cs.CL2026

When Tokenization is Secretly Output Supervision

Tanja Baeumel, Josef van Genabith, Simon Ostermann

Tokenization in language models is treated by default as an input preprocessing decision. We argue that this framing is incomplete: in autoregressive models, tokenizer granularity…

cs.CL2026

Separating Syntax from Language: A Mechanistic Account of Translation in Multilingual LLMs

Mikhail Sonkin, Tanja Baeumel, Daniil Gurgurov +2

Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they transform representations from o…

cs.CL20261 cited

A Sovereign, Open-Source Foundation Model for German and English

Soofi-Team, :, Benedikt Droste +30

We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B…

cs.CL2026

Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation

Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin +1

Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated data can improve downstream task per…