36 citations · 42 across the 11 of their papers we have counts for
11 papers
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…
The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
Debayan Banerjee, Pranav Ajit Nair, Ricardo Usbeck +1
In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowle…
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
Debayan Banerjee, Pranav Ajit Nair, Ricardo Usbeck +1
In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model…
Modern Baselines for SPARQL Semantic Parsing
Debayan Banerjee, Pranav Ajit Nair, Jivat Neet Kaur +2
In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity an…
SCoT: Sense Clustering over Time: a tool for the analysis of lexical change
Christian Haase, Saba Anwar, Seid Muhie Yimam +2
We present Sense Clustering over Time (SCoT), a novel network-based tool for analysing lexical change. SCoT represents the meanings of a word as clusters of similar words. It visua…
On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings
Abhik Jana, Dmitry Puzyrev, Alexander Panchenko +3
The compositionality degree of multiword expressions indicates to what extent the meaning of a phrase can be derived from the meaning of its constituents and their grammatical rela…