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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…

The rapid advancements in the field of Artificial Intelligence (AI) have led to the introduction of Large Language Models (LLMs). These highly capable models can generate human-like text and can perform tasks including question answering, text summarization, language translation, and code completion.  AI systems, particularly LLMs, can behave dishonestly strategically, much like how people can…

Understanding why things happen, known as causal inference, is a key part of human intelligence. There are two main ways we gain this ability: one is through what we’ve learned from experience, like knowing that touching a hot stove causes burns based on common sense; the other is through pure causal reasoning, where we formally…