The study of evolution by natural selection at a molecular level has witnessed remarkable progress with the advent of genomic technologies. Traditionally, researchers focused on observable traits; however, gene expression offers deeper insights into selection pressures, bridging the gap between genomic data and macro traits. A recent study used RNA sequencing to analyze gene expression…
Large Language Models (LLMs) such as GPT-3 and Llama face significant inefficiencies during large-scale training due to hardware failures and network congestion. These issues can lead to a substantial waste of GPU resources and extended training durations. Existing methods to address these challenges, which involve basic fault tolerance and traffic management strategies, are often inefficient…
Natural Language Processing (NLP) aims to enable computers to understand and generate human language, facilitating human-computer interaction. Despite advancements in NLP, large language models (LLMs) often fall short when it comes to complex planning tasks, such as decision-making and organizing actions - abilities crucial in a diverse array of applications from daily tasks to strategic…
Omost is an innovative project aimed at improving the image generation capabilities of Large Language Models (LLMs). The technology essentially converts the programming ability of an LLM into advanced image composition skills. The concept behind Omost's name is two-fold; firstly, after its use, the produced image should be 'almost' perfect. Secondly, 'O' stands for 'omni,'…
The introduction of large language models (LLMs) such as Llama, PaLM, and GPT-4 has transformed the world of natural language processing (NLP), elevating the capabilities for text generation and comprehension. However, a key issue with these models is their tendency to produce hallucinations - generating content that is factually incorrect or inconsistent with the input…