Introduction
Llama 2, a large language model, is a product of Microsoft and Meta, two major tech companies that typically go head-to-head. It’s a new version of the early 2023 release of Meta’s Llama 1. Imagine it as Meta’s take on fancy text tools like Google’s PaLM 2, OpenAI’s GPT-4, and Anthropic’s Claude 2. Llama 2 is more intelligent than Llama 1 since it was trained to utilize a lot of data from the internet. It can comprehend twice as much text simultaneously (4000 words) and learn from 40% more data.
Like GPT-3 and PaLM-2, Llama 2 is a family of LLMs (large language models). Even though they all perform comparable tasks, you might not notice the slight changes if you are not an expert in AI. These programs were developed and function roughly identically. They employ the same techniques, such as learning from a large amount of data before making precise adjustments, and they share a structure known as a transformer.
How does Llama 2 work?
Llama 2 was developed by training it on a massive dataset of 2 trillion “tokens”. These tokens are language fragments, such as words or significant clauses in phrases. This training material came from freely accessible resources like Wikipedia, Common Crawl, and literature in the public domain. This training was designed to help Llama 2 comprehend text and make meaningful predictions about what might happen next. For instance, if it observed that the terms “Apple” and “iPhone” frequently appeared together, it might infer that these two keywords are connected concepts as opposed to those that are unrelated, such as “apple”, “banana”, and “fruit.”
However, using the vast and sometimes unpredictable content of the open internet to train an AI model might result in issues like biased or inappropriate language. The developers used a number of strategies to address this. Reinforcement learning with human feedback (RLHF) is one method where human testers rank various AI responses to direct the AI toward Producing useful and appropriate outputs. The AI could learn to steer clear of creating bad information in this way.
Additionally, the AI system had to be tuned in order to have discussions that sounded natural. Its conversational skills were honed using specialized data so that it could react to interactions more humanely.
Llama 2 Use Cases
Text Generation:
- Generate text that is safe and harmless.
- Useful for writing social media posts, YouTube scripts, stories, poems, novels, blog posts, and essays.
- Llama 2 generates unique content depending on your input of a few words or sentences and its training data.
Text Summarization:
- Effectively summarizes the texts presented without losing important details.
- Particularly effective for generating English-language content.
- For rapid summaries, paste your English text and use the input type “Summarize the following paragraph: [Your Text]”.
Text Extender:
- Improve the quality of existing sentences or paragraphs.
- Llama 2 extends the existing text by utilizing natural language processing technology.
- For text extension, you can prompt Llama 2 “Write a lengthy poem about [Your Title]”.
Is Llama 2 safe to use?
Llama 2 is like a tool that is designed to answer queries in a human-sounding language. It is used to create Chatbots like ChatGPT and Google Bard. These chatbots are often safe to use, but the companies that make them could train them based on what you say. Bad people occasionally utilize these chatbots to steal information. It’s therefore advised to refrain from sharing personal information with them.
How accurate is Llama 2?
The kind of queries asked and the data it was trained on will determine how accurately a chatbot responds to them. Different chatbots, particularly those that answer simple inquiries, like Google Bard, ChatGPT, and Bing Chat, do not always provide accurate responses. But they are better at answering queries about more intricate coding.
We asked the chatbot to explain Llama 2’s capabilities, and it did so with a good explanation. But when we asked the same question again, it responded that “Llama 2” can be a phrase connected to something negative or incorrect. It made mention of the fact that it cannot provide information on matters that encourage harm, impermissible behavior, or unfairness. Instead, it seeks to offer solutions that are constructive and beneficial. It inquired as to whether there was anything further it could do for us.
