Moreover, such models suffer from overfitting such that Transformer-based solutions are the backbone of current once given a video from an unseen context or distribution state-of-the-art methods for language generation, image the quality and accuracy of the description drops, as our and video classification, segmentation, action and object evaluations prove. On the other hand, VLLMs have shown recognition, among many others. Interestingly enough, impressive results, being capable of generating long, rich while these state-of-the-art methods produce impressive results descriptions of videos. Unfortunately VLLMs still share in their respective domains, the problem of understanding some of the same weaknesses as previous methods: they are the relationship between vision and language is largely unexplainable and they still rely on sampling frames still beyond our reach. In this work, we propose a common to process a video. Moreover, top-performing models such ground between vision and language based on events as GPT, Claude or Gemini are not open and are only accessible in space and time in an explainable and programmatic way, via an paid API. to connect learning-based vision and language state of the We argue that one of the main reasons why this interdisciplinary art models and provide a solution to the long standing problem cross-domain task is still far from being solved is of describing videos in natural language. We validate that we still lack an explainable way to bridge this apparently that our algorithmic approach is able to generate coherent, insurmountable gap. Explainability could provide a rich and relevant textual descriptions on videos collected more analytical and stage-wise way to make the transition from a variety of datasets, using both standard metrics (e.g. from vision to language that is both trustworthy and makes Bleu, ROUGE) and the modern LLM-as-a-Jury approach.

In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English; hence, their performance in English greatly exceeds other languages. To our knowledge, we are the first to collect and translate a large collection of texts, instructions, and benchmarks and train, evaluate, and release open-source LLMs tailored for Romanian. We evaluate our methods on four different categories, including academic benchmarks, MT-Bench (manually translated), and a professionally built historical, cultural, and social benchmark adapted to Romanian. We argue for the usefulness and high performance of RoLLMs by obtaining state-of-the-art results across the board. We publicly release all resources (i.e., data, training and evaluation code, models) to support and encourage research on Romanian LLMs while concurrently creating a generalizable recipe, adequate for other low or less-resourced languages.

In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the training data is in English. Hence, their performance in English greatly exceeds their performance in other languages. This document presents our approach to training and evaluating the first foundational and chat LLM specialized for Romanian.

In recent years,the entire field of Natural Language Processing (NLP) has enjoyed amazing novel results achieving almost human-like performance on a variety of tasks. Legal NLP domain has also been part of this process, as it has seen an impressive growth. However, general-purpose models are not readily applicable for legal domain. Due to the nature of the domain (e.g. specialized vocabulary, long documents) specific models and methods are often needed for Legal NLP. In this work we investigate both specialized and general models for predicting the final ruling of a legal case, task known as Legal Judgment Prediction (LJP). We particularly focus on methods to extend to sequence length of Transformer-based models to better understand the long documents present in legal corpora. Extensive experiments on 4 LJP datasets in Romanian, originating from 2 sources with significantly different sizes and document lengths, show that specialized models and handling long texts are critical for a good performance.

Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two domains. In this work we come to address this limitation and propose the Graph of Events in Space and Time (GEST), by which we can represent, create and explain, both visual and linguistic stories. We provide a theoretical justification of our model and an experimental validation, which proves that GEST can bring a solid complementary value along powerful deep learning models. In particular, GEST can help improve at the content-level the generation of videos from text, by being easily incorporated into our novel video generation engine. Additionally, by using efficient graph matching techniques, the GEST graphs can also improve the comparisons between texts at the semantic level.

One of the essential human skills is the ability to seamlessly build an inner representation of the world. By exploiting this representation, humans are capable of easily finding consensus between visual, auditory and linguistic perspectives. In this work, we set out to understand and emulate this ability through an explicit representation for both vision and language - Graphs of Events in Space and Time (GEST). GEST alows us to measure the similarity between texts and videos in a semantic and fully explainable way, through graph matching. It also allows us to generate text and videos from a common representation that provides a well understood content. In this work we show that the graph matching similarity metrics based on GEST outperform classical text generation metrics and can also boost the performance of state of art, heavily trained metrics.