In today’s data-driven world, data analysts play a crucial role in various domains. Businesses use data extensively to inform strategy, enhance operations, and obtain a competitive edge. Professionals known as data analysts enable this by turning complicated raw data into understandable, useful insights that help in decision-making. They navigate the whole data analysis cycle, from…
The use of large language models like GPT-4o and GPT-4o-mini has brought significant advancements in natural language processing, enabling high-quality response generation, document rewriting, and productivity enhancements across numerous applications. However, one of the biggest challenges these models face is latency. Whether it’s updating a blog post or refining lines of code, the lag associated…
In recent years, the field of text-to-speech (TTS) synthesis has seen rapid advancements, yet it remains fraught with challenges. Traditional TTS models often rely on complex architectures, including deep neural networks with specialized modules such as vocoders, text analyzers, and other adapters to synthesize realistic human speech. These complexities make TTS systems resource-intensive, limiting their…
Flow-based generative modeling stands out in computational science as a sophisticated approach that facilitates rapid and accurate inferences for complex, high-dimensional datasets. It is particularly relevant in domains requiring efficient inverse problem-solving, such as astrophysics, particle physics, and dynamical system predictions. In these fields, researchers work to understand and interpret complex data by developing models…
Foundation models hold promise in medicine, especially in assisting complex tasks like Medical Decision-Making (MDM). MDM is a nuanced process requiring clinicians to analyze diverse data sources—like imaging, electronic health records, and genetic information—while adapting to new medical research. LLMs could support MDM by synthesizing clinical data and enabling probabilistic and causal reasoning. However, applying…
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical challenge in this field is understanding and predicting the relationship between the number of demonstrations provided and the model’s performance improvement, known as the ICL curve. This relationship needs to be…
Large language models (LLMs) are getting better at scaling and handling long contexts. As they are being used on a large scale, there has been a growing demand for efficient support of high-throughput inference. However, efficiently serving these long-context LLMs presents challenges related to the key-value (KV) cache, which stores previous key-value activations to avoid…
Recent advancements in generative language modeling have propelled natural language processing, making it possible to create contextually rich and coherent text across various applications. Autoregressive (AR) models generate text in a left-to-right sequence and are widely used for tasks like coding and complex reasoning. However, these models face limitations due to their sequential nature, which…