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20242026
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cs.CL2026

LLM2Vec-Gen: Generative Embeddings from Large Language Models

Parishad BehnamGhader, Vaibhav Adlakha, Fabian David Schmidt +3

Fine-tuning LLM-based text embedders via contrastive learning maps inputs and outputs into a new representational space, discarding the LLM's output semantics. We propose LLM2Vec-G…

cs.CL2025

ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance

Hannah Sterz, Fabian David Schmidt, Goran Glavaš +1

As they become increasingly multilingual, Large Language Models (LLMs) exhibit more language confusion, i.e., they tend to generate answers in a language different from the languag…

cs.CL2025

mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks

Luel Hagos Beyene, Vivek Verma, Min Ma +4

Large Language models (LLMs) have demonstrated impressive performance on a wide range of tasks, including in multimodal settings such as speech. However, their evaluation is often…

cs.CL2025

Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

Fabian David Schmidt, Ivan Vulić, Goran Glavaš +1

Spoken language understanding (SLU) is indispensable for half of all living languages that lack a formal writing system. Unlike for high-resource languages, for these languages, we…

cs.CL2025

On Generalization across Measurement Systems: LLMs Entail More Test-Time Compute for Underrepresented Cultures

Minh Duc Bui, Kyung Eun Park, Goran Glavaš +2

Measurement systems (e.g., currencies) differ across cultures, but the conversions between them are well defined so that humans can state facts using any measurement system of thei…

cs.CL2025

MVL-SIB: A Massively Multilingual Vision-Language Benchmark for Cross-Modal Topical Matching

Fabian David Schmidt, Florian Schneider, Chris Biemann +1

Existing multilingual vision-language (VL) benchmarks often only cover a handful of languages. Consequently, evaluations of large vision-language models (LVLMs) predominantly targe…