The efficient training of vision models is still a major challenge in AI because Transformer-based models suffer from computational bottlenecks due to the quadratic complexity of self-attention mechanisms. Also, the ViTs, although extremely promising results on hard vision tasks, require extensive computational and memory resources, making them impossible to use under real-time or resource-constrained conditions.… →

Datasets and pre-trained models come with intrinsic biases. Most methods rely on spotting them by analyzing misclassified samples in a semi-automated human computer validation. Deep neural networks, typically fine-tuned foundational models, are widely used in sectors like healthcare, finance, and criminal justice, where biased predictions can have serious societal impacts. These models often function as… →

Text embedding, a central focus within natural language processing (NLP), transforms text into numerical vectors capturing the essential meaning of words or phrases. These embeddings enable machines to process language tasks like classification, clustering, retrieval, and summarization. By structuring data in vector form, embeddings provide a scalable and effective way for machines to interpret and… →

Meta has recently released NotebookLlama, an open version of Google’s NotebookLM that empowers researchers and developers with accessible, scalable solutions for interactive data analysis and documentation. NotebookLlama integrates large language models directly into an open-source notebook interface, similar to Jupyter or Google Colab, allowing users to interact with a trained LLM as they would with… →

The rise of the information era has brought an overwhelming amount of data in varied formats. Documents, presentations, and images are generated at an astonishing rate across multiple languages and domains. However, retrieving useful information from these diverse sources presents a significant challenge. Conventional retrieval models, while effective for text-based queries, struggle with complex multimodal… →

Large Language Models (LLMs) have demonstrated impressive capabilities in handling knowledge-intensive tasks through their parametric knowledge stored within model parameters. However, the stored knowledge can become inaccurate or outdated, leading to the adoption of retrieval and tool-augmented methods that provide external contextual knowledge. A critical challenge emerges when this contextual knowledge conflicts with the model’s… →

Large language models (LLMs) have transformed fields ranging from customer service to medical assistance by aligning machine output with human values. Reward models (RMs) play an important role in this alignment, essentially serving as a feedback loop where models are guided to provide human-preferred responses. While many advancements have optimized these models for English, a… →