Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates extent knowledge, awareness, reasoning abilities encoded within such pretrained LLMs. With a focus on autoregressive models, we devise experimental approaches (i) probing LLMs geo-coordinates assess (ii) using non-geospatial...

Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in children's stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of generated by LLMs using various measures, and compare contrast our results with both old new better assess significance. Our findings suggest that still struggle generate at level quality nuance found actual stories.

Recent advancements in morpheme segmentation primarily emphasize word-level segmentation, often neglecting the contextual relevance within sentence. In this study, we redefine task as a sequence-to-sequence problem, treating entire sentence input rather than isolating individual words. Our findings reveal that multilingual model consistently exhibits superior performance compared to monolingual counterparts. While our did not surpass of current state-of-the-art, it demonstrated comparable...

Understanding urban mobility patterns and analyzing how people move around cities helps improve the overall quality of life supports development more livable, efficient, sustainable areas. A challenging aspect this work is collection data by means user tracking or travel surveys, given associated privacy concerns, noncompliance, high cost. This proposes an innovative AI-based approach for synthesizing surveys prompting large language models (LLMs), aiming to leverage their vast amount...

Large Language Models (LLMs) have shown a tremendous capacity for generating literary text. However, their effectiveness in children's stories has yet to be thoroughly examined. In this study, we evaluate the trustworthiness of generated by LLMs using various measures, and compare contrast our results with both old new better assess significance. Our findings suggest that still struggle generate at level quality nuance found actual

Autoregressive Large Language Models have transformed the landscape of Natural Processing. Pre-train and prompt paradigm has replaced conventional approach pre-training fine-tuning for many downstream NLP tasks. This shift been possible largely due to LLMs innovative prompting techniques. shown great promise a variety tasks owing their vast parameters huge datasets that they are pre-trained on. However, in order fully realize potential, outputs must be guided towards desired outcomes....

The paper presents a modular approach for the estimation of leading vehicle's velocity based on non-intrusive stereo camera where SiamMask is used vehicle tracking, Kernel Density estimate (KDE) to smooth distance prediction from disparity map, and LightGBM estimation. Our yields an RMSE 0.416 which outperforms baseline 0.582 SUBARU Image Recognition Challenge

Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates extent knowledge, awareness, reasoning abilities encoded within such pretrained LLMs. With a focus on autoregressive models, we devise experimental approaches (i) probing LLMs geo-coordinates assess (ii) using non-geospatial...