Generative AI models have become highly prominent in recent years for their ability to generate new content based on existing data, such as text, images, audio, or video. A specific sub-type, diffusion models, produces high-quality outputs by transforming noisy data into a structured format. Even though the model is significantly advanced, it still lacks control… →

Despite recent advances in multimodal large language models (MLLMs), the development of these models has largely centered around English and Western-centric datasets. This emphasis has resulted in a significant gap in linguistic and cultural representation, with many languages and cultural contexts around the world remaining underrepresented. Consequently, existing models often perform poorly in multilingual environments… →

In recent years, large language models (LLMs) have demonstrated significant progress in various applications, from text generation to question answering. However, one critical area of improvement is ensuring these models accurately follow specific instructions during tasks, such as adjusting format, tone, or content length. This is particularly important for industries like legal, healthcare, or technical… →

The generative AI market has expanded exponentially, yet many existing models still face limitations in adaptability, quality, and computational demands. Users often struggle to achieve high-quality output with limited resources, especially on consumer-grade hardware. Addressing these challenges requires solutions that are both powerful and adaptable for a wide range of users—from individual creators to large… →

Retrieval-Augmented Generation (RAG) is a growing area of research focused on improving the capabilities of large language models (LLMs) by incorporating external knowledge sources. This approach involves two primary components: a retrieval module that finds relevant external information and a generation module that uses this information to produce accurate responses. RAG is particularly useful in… →

Alignment with human preferences has led to significant progress in producing honest, safe, and useful responses from Large Language Models (LLMs). Through this alignment process, the models are better equipped to comprehend and represent what humans think is suitable or important in their interactions. But, maintaining LLMs’ advancement in accordance with these inclinations is a… →