6 citations · 6 across the 6 of their papers we have counts for
10 papers · 1 filter
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…
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Gregor Geigle, Florian Schneider, Carolin Holtermann +4
Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output i…
Large Language Models Are Overparameterized Text Encoders
Thennal D K, Tim Fischer, Chris Biemann
Large language models (LLMs) demonstrate strong performance as text embedding models when finetuned with supervised contrastive training. However, their large size balloons inferen…
Dataset of Quotation Attribution in German News Articles
Fynn Petersen-Frey, Chris Biemann
Extracting who says what to whom is a crucial part in analyzing human communication in today's abundance of data such as online news articles. Yet, the lack of annotated data for t…
On Zero-Shot Counterspeech Generation by LLMs
Punyajoy Saha, Aalok Agrawal, Abhik Jana +2
With the emergence of numerous Large Language Models (LLM), the usage of such models in various Natural Language Processing (NLP) applications is increasing extensively. Counterspe…
Probing Large Language Models from A Human Behavioral Perspective
Xintong Wang, Xiaoyu Li, Xingshan Li +1
Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, the understanding of their prediction processes and internal mechanisms, such as f…