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In 3D scene generation, a captivating challenge is the seamless integration of new objects into pre-existing 3D scenes. The ability to modify these complex digital environments is crucial, especially when aiming to enhance them with human-like creativity and intention. While adept at altering scene styles and appearances, earlier methods falter in inserting new objects consistently…

Navigating the intricate coding landscape often presents developers with a recurrent challenge – the disruptive back-and-forth between their code and external language models. This process involves a tedious dance of copying, pasting, and editing, leading to a fractured coding flow. While some developers have explored the use of ChatGPT during coding, the constant context-switching required…

Reinforcement learning (RL) has applications in various fields, and one such important application can be found in aligning language models with human values. Reinforcement learning from Human Feedback (RLHF) emerges as a pivotal technology in this alignment field. One of the challenges pertains to the limitations of reward models that serve as proxies for human…

Much of the modern Artificial Intelligence (AI) models are powered by enormous training data, ranging from billions to even trillions of tokens, which is only possible with web-scraped data. This web content is translated into numerous languages, and the quality of these multi-way translations suggests they were primarily created using Machine Translation (MT). This research…

Object detection plays a vital role in multi-modal understanding systems, where images are input into models to generate proposals aligned with text. This process is crucial for state-of-the-art models handling Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC). OVD models are trained on base categories in zero-shot scenarios but must predict both…

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,…