Vision-language Models (VLMs), such as CLIP and SigLIP, have become the de facto standard for multimodal tasks, serving as essential building blocks for recent Multimodal Large Language Models, including LLaVA and PaliGemma. However, current evaluations for VLMs remain heavily anchored to ImageNet. In this paper, we question whether ImageNet’s coverage is still sufficiently challenging for modern VLMs, and investigate the impact of adding novel and varied concept categories, i.e., semantically grouped fine-grained synsets. To this end, we introduce Concept-pedia, a novel, large-scale, semantically-annotated multimodal resource covering more than 165,000 concepts. Leveraging a language-agnostic, automatic annotation pipeline grounded in Wikipedia, Concept-pedia expands the range of visual concepts, including diverse abstract categories. Building on Concept-pedia, we also present a manually-curated Visual Concept Recognition evaluation benchmark, Concept-10k, that spans thousands of concepts across a wide range of categories. Our experiments show that current models, although excelling on ImageNet, struggle with Concept-10k. Not only do these findings highlight a persistent bias toward ImageNet-centric concepts, but they also underscore the urgent need for more representative benchmarks. By offering a broader and semantically richer testbed, Concept-10k aims to support the development of multimodal systems that better generalize to the complexities of real-world visual concepts.

The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not optimized for non-English languages, leading to inefficient encoding (high token “fertility”) and slower inference speed.In this work, we thoroughly compare a variety of vocabulary adaptation techniques for optimizing English LLMs for the Italian language, and put forward Semantic Alignment Vocabulary Adaptation (SAVA), a novel method that leverages neural mapping for vocabulary substitution. SAVA achieves competitive performance across multiple downstream tasks, enhancing grounded alignment strategies. We adapt two LLMs: Mistral-7B-v0.1, reducing token fertility by 25%, and Llama-3.1-8B, optimizing the vocabulary and reducing the number of parameters by 1 billion. We show that, following the adaptation of the vocabulary, these models can recover their performance with a relatively limited stage of continual training on the target language. Finally, we test the capabilities of the adapted models on various multi-choice and generative tasks.

Sentence alignment – establishing links between corresponding sentences in two related documents – is an important NLP task with several downstream applications, such as machine translation (MT). Despite the fact that existing sentence alignment systems have achieved promising results, their effectiveness is based on auxiliary information such as document metadata or machine-generated translations, as well as hyperparameter-sensitive techniques. Moreover, these systems often overlook the crucial role that context plays in the alignment process. In this paper, we address the aforementioned issues and propose CroCoAlign: the first context-aware, end-to-end and fully neural architecture for sentence alignment. Our system maps source and target sentences in long documents by contextualizing their sentence embeddings with respect to the other sentences in the document. We extensively evaluate CroCoAlign on a multilingual dataset consisting of 20 language pairs derived from the Opus project, and demonstrate that our model achieves state-of-the-art performance. To ensure reproducibility, we release our code and model checkpoints at https://github.com/Babelscape/CroCoAlign.

Word Sense Disambiguation (WSD) is the task of associating a word in a given context with its most suitable meaning among a set of possible candidates. While the task has recently witnessed renewed interest, with systems achieving performances above the estimated inter-annotator agreement, at the time of writing it still struggles to find downstream applications. We argue that one of the reasons behind this is the difficulty of applying WSD to plain text. Indeed, in the standard formulation, models work under the assumptions that a) all the spans to disambiguate have already been identified, and b) all the possible candidate senses of each span are provided, both of which are requirements that are far from trivial. In this work, we present a new task called Word Sense Linking (WSL) where, given an input text and a reference sense inventory, systems have to both identify which spans to disambiguate and then link them to their most suitable meaning.We put forward a transformer-based architecture for the task and thoroughly evaluate both its performance and those of state-of-the-art WSD systems scaled to WSL, iteratively relaxing the assumptions of WSD. We hope that our work will foster easier integration of lexical semantics into downstream applications.