In the rapidly evolving landscape of machine learning and artificial intelligence, understanding the fundamental representations within transformer models has emerged as a critical research challenge. Researchers are grappling with competing interpretations of what transformers represent—whether they function as statistical mimics, world models, or something more complex. The core intuition suggests that transformers might capture the… →
In large language models (LLMs), “hallucination” refers to instances where models generate semantically or syntactically plausible outputs but are factually incorrect or nonsensical. For example, a hallucination occurs when a model provides erroneous information, such as stating that Addison’s disease causes “bright yellow skin” when, in fact, it causes fatigue and low blood pressure. This… →
As AI systems become integral to daily life, ensuring the safety and reliability of LLMs in decision-making roles is crucial. While LLMs have shown impressive performance across various tasks, their ability to operate safely and cooperate effectively in multi-agent environments still needs to be explored. Cooperation is critical in scenarios where agents work together to… →
AI advancements have led to the incorporation of a large variety of datasets for multimodal models, allowing for a more comprehensive understanding of complex information and a substantial increase in accuracy. Leveraging their advantages, multimodal models find applications in healthcare, autonomous vehicles, speech recognition, etc. However, the large data requirement of these models has led… →
Generative models have emerged as great tools for synthesizing complex data and enabling sophisticated industry predictions. In recent years, their application has expanded beyond NLP and media generation to fields like finance, where the challenges of intricate data streams and real-time analysis demand innovative solutions. Generative foundation models thrive on three primary elements: A large… →
Autoregressive models are used to generate sequences of discrete tokens. The next token is conditioned by the preceding tokens in a given sequence in the approach. Recent research showed that generating sequences of continuous embeddings autoregressively is also feasible. However, such Continuous Autoregressive Models (CAMs) generate these embeddings similarly sequentially, but they face challenges such… →
The field of natural language processing (NLP) has grown rapidly in recent years, creating a pressing need for better datasets to train large language models (LLMs). Multilingual models, in particular, require datasets that are not only large but also diverse and carefully curated to capture the nuances of many different languages. Existing resources like CC-100,… →