Omost is an innovative project designed to enhance the image generation capabilities of large language models (LLMs) by converting their coding proficiency into advanced image composition skills. Pronounced, “almost,” the name Omost symbolizes two key ideas: first, after using Omost, the image will be “almost” perfect; second, “O” stands for “omni” (multi-modal), and “most” signifies… →

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP), particularly in Question Answering (QA). However, hallucination remains a significant obstacle as LLMs may generate factually inaccurate or ungrounded responses. Studies reveal that even state-of-the-art models like GPT-4 struggle with accurately answering questions involving changing facts or less popular entities. Overcoming hallucinations is crucial for… →

Natural language processing (NLP) involves using algorithms to understand and generate human language. It is a subfield of artificial intelligence that aims to bridge the gap between human communication and computer understanding. This field covers language translation, sentiment analysis, and language generation, providing essential tools for technological advancements and human-computer interaction. NLP’s ultimate goal is… →

We all know AI is getting smarter every day, but you’ll never guess what these researchers just accomplished. A team from the University of Illinois has unleashed AI agents that can autonomously hack websites and exploit real-world zero-day vulnerabilities – security holes that even the developers don’t know about yet. That’s right, the age of… →

Pre-trained language model development has advanced significantly in recent years, especially with the advent of large-scale models. For languages such as English, there is no shortage of open-source chat models. However, the Chinese language has not seen equivalent progress. To bridge this gap, several Chinese models have been introduced, showcasing innovative approaches and achieving remarkable… →

The emergence of large language models (LLMs) such as Llama, PaLM, and GPT-4 has revolutionized natural language processing (NLP), significantly advancing text understanding and generation. However, despite their remarkable capabilities, LLMs are prone to producing hallucinations, content that is factually incorrect or inconsistent with user inputs. This phenomenon substantially challenges its reliability in real-world applications,… →

Large language models (LLMs) have shown promise in powering autonomous agents that control computer interfaces to accomplish human tasks. However, without fine-tuning on human-collected task demonstrations, the performance of these agents remains relatively low. A key challenge lies in developing viable approaches to build real-world computer control agents that can effectively execute complex tasks across… →