15 citations · 17 across the 9 of their papers we have counts for
11 papers
From Tone to Trajectory: Continuous Sentiment and the Shape of Monetary Policy Communication
Martin Feldkircher, Márton Kardos, Kristoffer Laigaard Nielbo
Central bank press conferences are not merely information releases --- they are structured narratives. We study whether the shape of sentiment within a statement, not just its aver…
MVEB: Massive Video Embedding Benchmark
Adnan El Assadi, Roman Solomatin, Isaac Chung +13
We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classificati…
Naturalistic measure of social norms alignment
Yevhen Kostiuk, Kenneth Enevoldsen, Peter Bjerregaard Vahlstrup +2
Social norms reflect shared expectations on acceptable behavior. Measuring social norms alignment remains challenging, with existing approaches typically relying on artificial clos…
Improving reasoning at inference time via uncertainty minimisation
Nicolas Legrand, Kenneth Enevoldsen, Márton Kardos +1
Large language models (LLMs) now exhibit strong multi-step reasoning abilities, but existing inference-time scaling methods remain computationally expensive, often relying on exten…
Dynaword: From One-shot to Continuously Developed Datasets
Kenneth Enevoldsen, Kristian Nørgaard Jensen, Jan Kostkan +14
Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguousl…
Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks
Isaac Chung, Imene Kerboua, Marton Kardos +2
The Massive Text Embedding Benchmark (MTEB) has become a standard evaluation platform for text embedding models. While previous work has established the core benchmark methodology,…
topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation
Márton Kardos, Kenneth C. Enevoldsen, Kristoffer Laigaard Nielbo
Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. The…
MIEB: Massive Image Embedding Benchmark
Chenghao Xiao, Isaac Chung, Imene Kerboua +7
Image representations are often evaluated through disjointed, task-specific protocols, leading to a fragmented understanding of model capabilities. For instance, it is unclear whet…
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen, Isaac Chung, Imene Kerboua +83
Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more co…
The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding
Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff +1
The evaluation of English text embeddings has transitioned from evaluating a handful of datasets to broad coverage across many tasks through benchmarks such as MTEB. However, this…
-- Semantic Signal Separation
Márton Kardos, Jan Kostkan, Arnault-Quentin Vermillet +3
Topic models are useful tools for discovering latent semantic structures in large textual corpora. Recent efforts have been oriented at incorporating contextual representations in…