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Privacy concerns have become a significant issue in AI research, particularly in the context of Large Language Models (LLMs). The SAFR AI Lab at Harvard Business School was surveyed to explore the intricate landscape of privacy issues associated with LLMs. The researchers focused on red-teaming models to highlight privacy risks, integrate privacy into the training…

In artificial intelligence, the challenge arises when multiple AI agents need to work together seamlessly to tackle complex tasks. This collaborative intelligence is essential for building intelligent assistant platforms, automated customer service ensembles, or multi-agent research teams. Existing solutions, like Autogen and ChatDev, have their strengths, but they come with limitations, such as complex programming…

In the landscape of text-to-image models, the demand for high-quality visuals has surged. However, these models often need to grapple with resource-intensive training and slow inference, hindering their real-time applicability. In response, this paper introduces PIXART-δ, an advanced iteration that seamlessly integrates Latent Consistency Models (LCM) and a custom ControlNet module into the existing PIXART-α…

Large Language Models (LLMs) signify a remarkable advance in natural language processing and artificial intelligence. These models, exemplified by their ability to understand and generate human language, have revolutionized numerous applications, from automated writing to translation. However, their complexity and potential for misuse, such as spreading misinformation or biased content, have raised significant concerns about…

The trend of employing large language models (LLMs) for code generation is rapidly gaining momentum in software development. However, the lack of robust mechanisms for validating the accuracy of the generated code may result in numerous adverse outcomes. The absence of effective methods for ensuring correctness raises significant risks, including but not limited to bugs,…

Person Re-identification (Person Re-ID) in Machine Learning uses deep learning models like convolutional neural networks to recognize and track individuals across different camera views, holding promise for surveillance and public safety but raising significant privacy concerns. The technology’s capacity to track people across locations increases surveillance and security risks, along with potential privacy issues like…

In a recent tweet from the founder of Dataquest.io, Vik Paruchuri recently publicized the launch of a multilingual document OCR toolkit, Surya. The framework can efficiently detect line-level bboxes and column breaks in documents, scanned images, or presentations. The existing text detection models like Tesseract work at the word or character level, while this open-source…

Large language models (LLMs) face a hurdle in handling long contexts due to their constrained window length. Although the context window length can be extended through fine-tuning, this incurs significant training and inference time costs, adversely affecting the LLM’s core capabilities. Current LLMs, such as Llama-1 and Llama-2, have fixed context lengths, hindering real-world applications….

Machine translation, an integral branch of Natural Language Processing, is continually evolving to bridge language gaps across the globe. One persistent challenge is the translation of low-resource languages, which often need more substantial data for training robust models. Traditional translation models, primarily based on large language models (LLMs), perform well with languages abundant in data…

There has been a notable discrepancy between the global distribution of language speakers and the predominant language of online material, which is English. Even while English is used in up to 60% of internet information, only 18.8% of people worldwide speak it, and just 5.1% of people use it as their first language. For non-English…