Large Language Models (LLMs) such as GPT 3.5 and GPT 4 have recently garnered substantial attention in the Artificial Intelligence (AI) community for their ability to process vast amounts of data, detect patterns, and simulate human-like language in response to prompts. These LLMs are capable of self-improvement over time, drawing upon new information and user…
Revealing the Power of Big Language Models: Improving Comment Creation in Computer Science Education
Large classroom sizes in computing education are making it crucial to use automation for student success. Automated feedback generation tools are becoming increasingly popular for their ability to rapidly analyze and test. Among these, large language models (LLMs) like GPT-3 are showing promise. However, concerns about their accuracy, reliability, and ethical implications do exist.
Historically, the…
The evaluation of artificial intelligence (AI) systems, particularly large language models (LLMs), has come to the fore in recent artificial intelligence research. Existing benchmarks, such as the original Massive Multitask Language Understanding (MMLU) dataset, have been found to inadequately capture the true potential of AI systems, largely due to their focus on knowledge-based questions and…
The assessment of artificial intelligence (AI) models, particularly large language models (LLMs), is a field of rapid research evolution. There is a growing focus on creating more rigorous benchmarks to assess these models' abilities across various complex tasks. Understanding the strengths and weaknesses of different AI systems through this field is crucial as it helps…
Transformer models have ushered in a new era of Natural Language Processing (NLP), but their high memory and computational costs often pose significant challenges. This has fueled the search for more efficient alternatives that uphold the same performance standards but require fewer resources. While some research has been conducted on Linear Transformers, the RWKV model,…
Large language models (LLMs) such as GPT-4, LLaMA, and PaLM are playing a significant role in advancing the field of artificial intelligence. However, the attention mechanism of these models relies on generating one token at a time, thus leading to high latency. To address this, researchers have proposed two approaches to efficient LLM inference, with…
Researchers from MIT and other institutions have developed a method that prevents large AI language machines from crashing during lengthy dialogues. The solution, known as StreamingLLM, tweaks the key-value cache (a sort of conversation memory) of large language models to ensure the first few data pieces remain in memory. Typically, once the cache's capacity is…
In our data-dominated age, data science is a crucial field that uses statistics, computer science, and domain knowledge to extract insights from vast lakes of information. As a beginner, diving into this field can seem overwhelming, but many structured courses can guide you through the essential concepts and skills. These programs are designed to be…
Data drift is a phenomena that impacts any AI model in current operation. It is essentially a change in the features distribution an AI model receives while it's in production, thereby leading to a decline in the model's performance. A visible impact in imaging AI, for instance, could be an algorithm becoming less reliable at…
The upcoming 2024 general elections in India, the world's largest democratic exercise involving over 960 million voters, are experiencing a significant transformation due to the influence of artificial intelligence (AI) and deep fakes. A new cohort of technologically adept political players is exploiting AI to create synthetic media aiming for political and commercial influence.
Among them…