Natural Language Processing (NLP) is rapidly evolving, with small efficient language models gaining relevance. These models, ideal for efficient inference on consumer hardware and edge devices, allow for offline applications and have shown significant utility when fine-tuned for tasks like sequence classification or question answering. They can often outperform larger models in specialized areas.
One…
The evolution of Large Language Models (LLMs) in artificial intelligence has spawned several sub-groups, including Multi-Modal LLMs, Open-Source LLMs, Domain-specific LLMs, LLM Agents, Smaller LLMs, and Non-Transformer LLMs.
Multi-Modal LLMs, such as OpenAI's Sora, Google's Gemini, and LLaVA, consolidate various types of input like images, videos, and text to perform more sophisticated tasks. OpenAI's Sora…
The paper discusses the challenge of ensuring that large language models (LLMs) generate accurate, credible, and verifiable responses. This is difficult as the current methods often require assistance due to errors and hallucinations, which results in incorrect or misleading information. To address this, the researchers introduce a new verification framework to improve the accuracy and…
In recent years, Large Language Models (LLMs) have gained prominence due to their exceptional text generation, analysis, and classification capabilities. However, their size, need for high processing power and energy, pose barriers to smaller businesses. As the rush for bigger models increases, an interesting trend is gaining momentum: the rise of Small Language Models (SLMs),…