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.

This blog post will discuss a new approach to training large language models (LLMs) called Reflexion . LLMs are a type of artificial intelligence (AI) that are trained on massive amounts of text data. This allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, traditional reinforcement learning methods can be challenging for LLMs because they require extensive training samples and fine-tuning. Reflexion addresses this challenge by using verbal reinforcement to help agents learn from their mistakes. Here's a breakdown of the key points: What is Reflexion? Reflexion is a novel framework that reinforces LLMs through linguistic feedback. Instead of using rewards or punishments, Reflexion agents receive textual summaries of their performance. How Does It Work? Reflexion agents interact with an environment and receive feedback on their actions. This feedback is then converted into natural...

The Race for the Top AI Assistant Meta has released its new AI assistant , powered by the Llama 3 model, which is now available across its apps, including Instagram, WhatsApp, Facebook and Messenger. With this launch, Meta is taking on ChatGPT and other AI competitors in the market. What Can Meta AI Do? Meta AI is designed to be a helpful assistant, aiding with tasks such as planning a night out, finding information for a test, or even helping with work emails. It can also generate images and GIFs based on text inputs, a feature called 'Imagine'. This feature is now faster and more advanced, producing images as you type. Global Reach Meta AI is now available in English in over a dozen countries outside of the US, including Australia, Canada, Ghana, Jamaica, and New Zealand, with more countries and languages to be added in the future. The Power Behind Meta AI: Llama 3 Llama 3 is Meta's latest open-source AI model, which the company claims outperforms competing models in its ...

In the realm of machine learning and natural language processing (NLP), understanding sequences is paramount. A sequence can be anything from a sentence in language, to medical signals, or even the waveform of speech. At its core, a sequence is an ordered collection of elements that conveys meaningful information. Consider the task of predicting the next word in a sentence, a classic sequence modeling problem. Given the context of a sentence like "This morning I took the dog for a walk," predicting the next word requires understanding the sequential flow of language. One approach is to use a fixed window, considering a subset of words to predict the next one. However, this method has limitations, particularly in capturing long-term dependencies. For instance, in a sentence like "In Finland, I had a great time and I learnt some of the _________ language," accurately predicting the missing word necessitates understanding information from both distant past and future w...