The digital age demands for automation and efficiency in the domain of software and applications. Automating repetitive coding tasks and reducing debugging time frees up programmers’ time for more strategic work. This can be especially beneficial for businesses and organizations that rely heavily on software development. The recently released AI-powered Python notebook Thread addresses the…
Large Language Models (LLMs) have taken over the Artificial Intelligence (AI) community in recent times. In a Reddit post, a user recently brought attention to the startling quantity of over 700,000 large language models on Hugging Face, which sparked an argument about their usefulness and potential. This article is based on a Reddit thread, and…
Controlling the language proficiency levels in texts generated by large language models (LLMs) is a significant challenge in AI research. Ensuring that generated content is appropriate for various proficiency levels is crucial for applications in language learning, education, and other contexts where users may not be fully proficient in the target language. Without effective proficiency…
Large Language Models (LLMs) have made substantial progress in the field of Natural Language Processing (NLP). By scaling up the number of model parameters, LLMs show higher performance in tasks such as code generation and question answering. However, most modern LLMs, like Mistral, Gemma, and Llama, are dense models, which means that during inference, they…
Large language models (LLMs) have enabled the creation of autonomous language agents capable of solving complex tasks in dynamic environments without task-specific training. However, these agents often face challenges when tasked with broad, high-level goals due to their ambiguous nature and delayed rewards. The impracticality of frequent model retraining to adapt to new goals and…
The Galileo Luna represents a significant advancement in language model evaluation. It is specifically designed to address the prevalent issue of hallucinations in large language models (LLMs). Hallucinations, or instances where models generate information not grounded in the retrieved context, pose a significant challenge in deploying language models in industry applications. The Galileo Luna is…
Recent advancements in LLMs have paved the way for developing language agents capable of handling complex, multi-step tasks using external tools for precise execution. While proprietary models or task-specific designs dominate existing language agents, these solutions often incur high costs and latency issues due to API reliance. Open-source LLMs focus narrowly on multi-hop question answering…
Developing large language models requires substantial investments in time and GPU resources, translating directly into high costs. The larger the model, the more pronounced these challenges become. Recently, Yandex has introduced a new solution: YaFSDP, an open-source tool that promises to revolutionize LLM training by significantly reducing GPU resource consumption and training time. In a…
In recent years, image generation has made significant progress due to advancements in both transformers and diffusion models. Similar to trends in generative language models, many modern image generation models now use standard image tokenizers and de-tokenizers. Despite showing great success in image generation, image tokenizers encounter fundamental limitations due to the way they are…
Researchers have drawn parallels between protein sequences and natural language due to their sequential structures, leading to advancements in deep learning models for both fields. LLMs have excelled in NLP tasks, and this success has inspired attempts to adapt them to understanding proteins. However, this adaptation faces a challenge: existing datasets need more direct correlations…