Future Tech Feed

The difference is a mere difference in the terminology used. When the model is trained on a large generic corpus, it is called 'pre-training'. When it is adapted to a particular task or dataset it is called 'fine-tuning'. Technically speaking, in either case ('pre-training or 'fine-tuning'), there are updates to the model weights. For example, usually, you can just take the pre-trained model and then fine-tune it for a specific task (such as classification, question-answering, etc.). However, if you find that the target dataset is from a specific domain, and you have a few unlabeled data that might help the model to adapt to the particular domain, then you can do an MLM or MLM+NSP 'fine-tuning' (unsupervised learning) (some researchers do call this as 'pre-training' especially when a huge corpus is used to train the model), followed by using the target corpus with target task fine-tuning.

Introduction: In the ever-evolving landscape of artificial intelligence (AI), advancements often come hand in hand with unforeseen vulnerabilities. Recently, researchers delved into a concerning exploit dubbed " many-shot jailbreaking ," shedding light on a potential threat lurking within large language models (LLMs). Understanding Many-Shot Jailbreaking: Many-shot jailbreaking capitalizes on the expanding context window of LLMs, which has grown exponentially in recent years. By embedding a series of faux dialogues within a single prompt, attackers can coerce LLMs into providing harmful responses, bypassing their safety protocols. This technique poses significant risks, from promoting violence and deception to facilitating illegal activities. The Impact and Implications: The implications of many-shot jailbreaking extend beyond mere exploitation; they raise critical questions about the safety and security of AI systems. As LLMs continue to grow more powerful, the potential ...

In the realm of artificial intelligence, whispers of a groundbreaking project known as Q* have been circulating, sparking intrigue and speculation among enthusiasts and skeptics alike. Q*, believed by many to be the brainchild of OpenAI, has emerged as a beacon of potential, heralding the dawn of a new era in AI development. The origins of Q* trace back to a tumultuous period within OpenAI, marked by internal strife and whispers of mutiny. It was during this time that murmurs of Q* first reached the ears of the public, shrouded in mystery and intrigue. Sam Altman, a figure of significance within the AI community, acknowledged the existence of Q*, albeit with a cryptic silence that only fueled the flames of curiosity. Speculation swirled as to the nature of Q*, with theories ranging from enhanced dialogue systems to the elusive pursuit of Artificial General Intelligence (AGI). Nathan Lambert, a voice amidst the speculation, posited that Q* could represent a breakthrou...