Natural language processing (NLP) has witnessed a profound impact of large language models (LLMs) that excel in a multitude of tasks. However, the limitation of LLMs in multilingual settings, particularly in underrepresented languages, remains a significant hurdle. This thesis aims to bridge the gap in NLP research and development by focusing on underrepresented languages. A comprehensive evaluation of LLMs is conducted to assess their capabilities in these languages, revealing the challenges of multilingual and multicultural generalization. Addressing the multilingual generalization gap, this thesis proposes data-and-compute-efficient methods to mitigate the disparity in LLM ability in underrepresented languages, allowing better generalization on underrepresented languages without the loss of task generalization ability. The proposed solutions cover cross-lingual continual instruction tuning, retrieval-based cross-lingual in-context learning, and in-context query alignment. Furthermore, a novel method to measure cultural values alignment between LLMs operating in different languages is proposed, ensuring cultural sensitivity and inclusivity. These contributions aim to enhance the multilingual and multicultural alignment of LLMs in underrepresented languages, ultimately advancing the NLP field toward greater equality and inclusiveness.

Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose significant security risks due to their tendency to memorize training data, leading to potential privacy breaches and copyright infringement. Accurate measurement of this memorization is essential to evaluate and mitigate these potential risks. However, previous attempts to characterize memorization are constrained by either using prefixes only or by prepending a constant soft prompt to the prefixes, which cannot react to changes in input. To address this challenge, we propose a novel method for estimating LLM memorization using dynamic, prefix-dependent soft prompts. Our approach involves training a transformer-based generator to produce soft prompts that adapt to changes in input, thereby enabling more accurate extraction of memorized data. Our method not only addresses the limitations of previous methods but also demonstrates superior performance in diverse experimental settings compared to state-of-the-art techniques. In particular, our method can achieve the maximum relative improvement of 112.75% and 32.26% over the vanilla baseline in terms of discoverable memorization rate for the text generation task and code generation task respectively.

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For example, a U-Net [31] model can be used for some studies have utilized manually annotated images for deep semantic segmentation, and a Convolutional Neural Network learning applications involving microplastics, their datasets are (CNN) can then classify the segmented pixels, as demonstrated not publicly accessible [22], [23], [25]. Notably, there is only in [22], [24]. It is also possible to perform instance segmentation one other open-source Scanning Electron Microscopy (SEM) directly from the start. For instance, a Mask R-CNN dataset on microplastics, presented in [24], which categorizes model can simultaneously identify regions of interest, classify particles by shape (e.g., fragments, fibers, and beads) and each detected object, and generate a mask for each instance, features a more limited size distribution. These contributions as shown by [23]. Additionally, Faster R-CNN, primarily used not only address the urgent environmental issue of microplastic for object detection, has been applied to microscopic images to contamination but also set a new benchmark for detecting and classify microplastics into two polymer types [25]. Given the analyzing microplastics in aquatic environments, paving the nature of our dataset, where overlapping and crowded MNPs way for future innovations in the field.

Large Language Models (LLMs) are increasingly being used to generate text across various languages, for tasks such as translation, customer support, and education. Despite these advancements, LLMs show notable gender biases in English, which become even more pronounced when generating content in relatively underrepresented languages like Hindi. This study explores implicit gender biases in Hindi text generation and compares them to those in English. We developed Hindi datasets inspired by WinoBias to examine stereotypical patterns in responses from models like GPT-4o and Claude-3 sonnet. Our results reveal a significant gender bias of 87.8% in Hindi, compared to 33.4% in English GPT-4o generation, with Hindi responses frequently relying on gender stereotypes related to occupations, power hierarchies, and social class. This research underscores the variation in gender biases across languages and provides considerations for navigating these biases in generative AI systems.

Knowledge Graphs (KGs) have been used to organize large datasets into structured, interconnected information, enhancing data analytics across various fields. In the legislative context, one potential natural application of KGs is modeling the intricate set of interconnections that link laws and their articles with each other and the broader legislative context. At the same time, the rise of large language models (LLMs) such as GPT has opened new opportunities in legal applications, such as text generation and document drafting. Despite their potential, the use of LLMs in legislative contexts is critical since it requires the absence of hallucinations and reliance on up-to-date information, as new laws are published on a daily basis. This work investigates how Legislative Knowledge Graphs and LLMs can synergize and support legislative processes. We address three key questions: the benefits of using KGs for legislative systems, how LLM can support legislative activities by ensuring an accurate output, and how we can allow non-technical users to use such technologies in their activities. To this aim, we develop Legis AI Platform, an interactive platform focused on Italian legislation that enhances the possibility of conducting legislative analysis and that aims to support lawmaking activities.

Nintendo and The Pokémon Company are suing the company behind the game, which fans dubbed "Pokémon with guns," for patent infringement. Palworld, colloquially known to fans as " Pokémon with guns," is in hot water. Nintendo and The Pokémon Company announced Thursday that they've filed a patent infringement lawsuit in Tokyo against Pocketpair, the company behind the game, claiming "infringes multiple patent rights." In, players catch creatures by weakening them and trapping them in Pal Spheres, similar to Poké Balls. Fans have also pointed out numerous similarities in design between Pals and Pokémon.

The return comes almost a year after a robotaxi dragged a pedestrian. Autonomous vehicle outfit Cruise is slowly returning to operation in California following an incident in which a pedestrian was by a robotaxi for approximately 20 feet in October 2023. The company on X that it is reintroducing human-operated mapping vehicles to the streets in Mountain View and Sunnyvale. Its next stated goal is to progress to supervised testing with up to 5 AVs later this fall. The past year has not been a pretty picture for Cruise, which was by GM in 2016.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Gabriel Zendejas Chavez leaves the federal courthouse in downtown Los Angeles. Indicted in 2018 in an investigation of the Mexican Mafia's rackets in L.A. County jails, Chavez pleaded guilty in federal court to a rarely filed charge called "misprision of a felony." A lawyer accused of helping members of the Mexican Mafia traffic drugs, collect extortion money and expose government informants pleaded guilty Thursday in a deal with prosecutors that may spare him prison time. Gabriel Zendejas Chavez, who was indicted in 2018 in an investigation of the Mexican Mafia's rackets in L.A. County jails, told U.S. District Judge George Wu he was guilty of a rarely filed charge of "misprision of a felony."

Prince Harry says his father King Charles is'great' following their first meeting in 19 months... which was over a cup of tea and just 55 minutes long A DEI mayor, an inconvenient crime and video they never wanted you to see: MAUREEN CALLAHAN knows why the Left has sympathy for that killer... but none for his victim Tragedy as Charlie Kirk's wife left behind with two young children after conservative activist is fatally shot Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Fox News reveals new lineup and elevates star White House reporter who's sparred with Trump I tried the 30 cent'miracle chill pill' before a big event.. now I'm taking it for everything Carlos Alcaraz's new girlfriend revealed after US Open triumph... amid Emma Raducanu rumors Kimberly Guilfoyle urged to'stop with the lips' as she shows off drastic new look Knifeman accused of stabbing Ukrainian refugee to death gives chilling reason for the attack... as he speaks for the first time from jail on the murder that shocked America We only had one symptom we dismissed... but then we were diagnosed with the rarest form of melanoma MSNBC sparks outrage for'disgusting' Charlie Kirk comments following Utah shooting I was thrilled to finally sit in the cuck chair... now I fear my fantasy has destroyed my marriage: DEAR JANE READ MORE: Top brain surgeon who says he went to heaven reveals what it's like A baffling new theory to explain human consciousness has suggested it comes from hidden dimensions and is not just brain activity. A physicist claimed that we plug in to these invisible planes of the universe when making art, practicing science, pondering philosophy or dreaming, and this could explain the phenomenon that has evaded scientific understanding for centuries. Michael Pravica, a professor of physics at the University of Nevada, Las Vegas, has based the wild idea on hyperdimensionality, the idea that the universe is made up of more dimensions than just the four we perceive: height, length width and time. But his theory is highly controversial, with one scientist saying that the cornerstone of Pravica's theory'borders on science fiction.' 'The sheer fact that we can conceive of higher dimensions than four within our mind, within our mathematics, is a gift... it's something that transcends biology,' Pravica told Popular Mechanics . Scientists have been attempting to explain human consciousness and its origins for hundreds of years - and the theories run the gamut.