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GPT-4 introduces a range of advancements that empower it to perform tasks previously unattainable by its predecessor, GPT-3.5. Here, Let’s explore ten functions that highlight the enhanced capabilities of GPT-4, showcasing its potential across various domains. Advanced Multimodal Capabilities GPT-4 integrates advanced multimodal functionalities, allowing it to process and generate content that includes text, images,…
Scaling Transformer-based models to over 100 billion parameters has led to groundbreaking results in natural language processing. These large language models excel in various applications, but deploying them efficiently poses challenges due to the sequential nature of generative inference, where each token’s computation relies on the preceding tokens. This necessitates meticulous parallel layouts and memory…
The release of the European LLM Leaderboard by the OpenGPT-X team presents a great milestone in developing and evaluating multilingual language models. The project, supported by TU Dresden and a consortium of ten partners from various sectors, aims to advance language models’ capabilities in handling multiple languages, thereby reducing digital language barriers and enhancing the…
Artificial intelligence (AI) has transformed traditional research, propelling it to unprecedented heights. However, it has a ways to go regarding other spheres of its application. A critical issue in AI is training models to perform causal reasoning. Traditional methods heavily depend on large datasets with explicitly marked causal relationships, which are often expensive and challenging…
Conversational Recommender Systems (CRS) are revolutionizing how users make decisions by offering personalized suggestions through interactive dialogue interfaces. Unlike traditional systems that present predetermined options, CRS allows users to dynamically input and refine their preferences, significantly reducing information overload. By incorporating feedback loops and advanced machine learning techniques, CRS provides an engaging and intuitive user…
The field of robotics is seeing transformative changes with the integration of generative methods like large language models (LLMs). These advancements enable the developing of sophisticated systems that autonomously navigate and adapt to various environments. The application of LLMs in robot design and control processes represents a significant leap forward, offering the potential to create…
Large Language Models (LLMs) have demonstrated remarkable proficiency in language generation tasks. However, their training process, which involves unsupervised learning from extensive datasets followed by supervised fine-tuning, presents significant challenges. The primary concern stems from the nature of pre-training datasets, such as Common Crawl, which often contain undesirable content. Consequently, LLMs inadvertently acquire the ability…
Language model adaptation is a crucial area in artificial intelligence, focusing on enhancing large pre-trained language models to work effectively across various languages. This research is vital for enabling these models to understand and generate text in multiple languages, which is essential for global AI applications. Despite the impressive performance of LLMs in English, their…
Large language models (LLMs) have shown exceptional capabilities in understanding and generating human language, making substantial contributions to applications such as conversational AI. Chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services. The effectiveness of these chatbots relies heavily on high-quality instruction-following data used in post-training, enabling them to…
Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT) and Time Between Tokens (TBT) do not capture the complete user experience during real-time interactions. This gap is critical in applications like chat and translation, where responsiveness directly affects user satisfaction.…