8 papers
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…
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…
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…
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding
Fabian David Schmidt, Ivan VuliÄ, Goran GlavaÅ¡ +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…
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…
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks
Adrian Rebmann, Fabian David Schmidt, Goran Glavaš +1
Large language models (LLMs) have shown to be valuable tools for tackling process mining tasks. Existing studies report on their capability to support various data-driven process a…