Multi-modal large language models (LLMs) have shown remarkable performance in various natural language processing tasks, including data extraction from documents. However, the accuracy of these models can be significantly affected by document in-plane rotation, also known as skew, a common issue in real-world scenarios for scanned documents. This study investigates the impact of document skew on the data extraction accuracy of three state-of-the-art multi-modal LLMs: Anthropic Claude V3 Sonnet, GPT-4-Turbo, and Llava:v1.6. We focus on extracting specific entities from synthetically generated sample documents with varying degrees of skewness. The results demonstrate that document skew adversely affects the data extraction accuracy of all the tested LLMs, with the severity of the impact varying across models. We identify the safe in-plane rotation angles (SIPRA) for each model and investigate the effects of skew on model hallucinations. Furthermore, we explore existing skew detection and correction mechanisms and discuss their potential limitations. We propose alternative approaches, including developing new multi-modal architectures that are inherently more robust to document skew and incorporating skewing techniques during the pre-training phase of the models. Additionally, we highlight the need for more comprehensive testing on a wider range of document quality and conditions to fully understand the challenges and opportunities associated with using multi-modal LLMs for information extraction in real-world scenarios.

Named Entity Recognition (NER) is one of the essential applications of Natural Language Processing (NLP). It is also an instrument that plays a significant role in many other NLP applications, such as Machine Translation (MT), Information Retrieval (IR), and Part of Speech Tagging (POST). Kurdish is an under-resourced language from the NLP perspective. Particularly, in all the categories, the lack of NER resources hinders other aspects of Kurdish processing. In this work, we present a data set that covers several categories of NEs in Kurdish (Sorani). The dataset is a significant amendment to a previously developed dataset in the Kurdish BLARK (Basic Language Resource Kit). It covers 11 categories and 33261 entries in total. The dataset is publicly available for non-commercial use under CC BY-NC-SA 4.0 license at https://kurdishblark.github.io/.

We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images covering 97 regions from 44 countries across 6 continents, with manually annotated 8-class land cover labels at a 0.25--0.5m ground sampling distance. Semantic segmentation models trained on the OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a variety of applications. We evaluate the performance of state-of-the-art methods for unsupervised domain adaptation and present challenging problem settings suitable for further technical development. We also investigate lightweight models using automated neural architecture search for limited computational resources and fast mapping. The dataset is available at https://open-earth-map.org.

If you make a purchase, My Modern Met may earn an affiliate commission. Please read our disclosure for more info. They are Roman emperors who lived long ago, but they remain embedded in popular imagination since the fall of the last ruler in 476 CE. They've been immortalized as busts and statues in museums, but a new project is helping to visualize what these people would've really looked like while alive. Daniel Voshart, a Toronto-based cinematographer and designer, has created photorealistic portraits of the 54 emperors of the Principate period (27 BC to 285 CE).