Unlocking the Full Potential of ChatGPT and Other Large Language Models Before It's Too Late
With the advent of ChatGPT, the tech industry witnessed a fierce competition among the giants. The race to integrate a similar large language model (LLM) into their search engines prompted a hasty approach, which may have resulted in overlooking the limitations like bias, privacy issues, and the challenges posed by abstract concepts or inadequate context.
Numerous researchers have come up with ways to jailbreak ChatGPT and Bing Chat, which essentially means that they were successful in bypassing the developer-imposed restrictions.
Advanced natural language processing models
The foundation of ChatGPT's design is based on large language models (LLMs), which is a subcategory of machine learning. LLMs use artificial intelligence (AI) to process natural language input on a wide range of topics.
These models are massive deep-neural-networks that undergo training on billions of pages of written material in a specific language to complete a given task, such as predicting the next word or sentence.
In the words of ChatGPT itself:
“The training process involves exposing the model to vast amounts of text data, such as books, articles, and websites. During training, the model adjusts its internal parameters to minimize the difference between the text it generates and the text in the training data. This allows the model to learn patterns and relationships in language, and to generate new text that is similar in style and content to the text it was trained on.”
Race to Incorporate Large Language Models (LLMs)
Large language models (LLMs) have become a battleground for tech giants as they compete to become the next ChatGPT. However, in their rush to incorporate LLMs into their products and gain an advantage over competitors, mistakes are likely to be made. While LLMs are a powerful tool, they are still a work in progress and should not be relied on completely. The hundreds of millions of dollars being invested in LLMs must eventually be recouped. In China, tech companies like Alibaba Group Holding, Tencent Holdings, Baidu, NetEase, and JD.com are racing to develop their own LLMs to stay competitive.
Presenting fiction as reality
Be aware that the principle of "garbage in - garbage out" still holds true for AI. If you instruct an AI to gather information on a non-existent topic, but it finds abundant information on a related topic, it may present that information as factual, even if it is not entirely accurate.
As OpenAI states in their disclaimer:
“While we have safeguards in place, the system may occasionally generate incorrect or misleading information and produce offensive or biased content. It is not intended to give advice.”
Bypassing Restrictions on ChatGPT and Other Large Language Models
While jailbreaking is still relatively easy, those with early access are trying to find the loopholes around the guardrails and providing developers with the opportunity to close those loopholes. Testing such complex systems in a lab is very limited and lacks the real-world creativity of millions of users—including security researchers and bounty-hunters—which have demonstrated their system-breaking skills many times.
Let us know in the comments what your experiences with LLMs are. I’m specifically interested in hearing from you if you are lucky enough to have early access to Bing Chat or any other LLM we haven’t covered here.
As jailbreaking remains a feasible option, early access holders are striving to uncover the gaps in the guardrails. Developers are then given a chance to address and fix these gaps. Nonetheless, testing complex systems in a lab restricts the creative ingenuity of millions of users, including security researchers and bounty-hunters, who have repeatedly demonstrated their abilities to break into such systems.
If you have had any encounters with LLMs, feel free to share your experiences in our Facebook group. We're particularly interested in hearing from those who have been granted early access to Bing Chat or any other LLM that we may not have mentioned. Join the discussion now and share your thoughts!
