While it is exciting to witness the significant progress made by large language models (LLMs) and generative AI in general, there are some caveats that DVC considers before fully embracing these technologies. 

One of the most prominent concerns is the issue of bias. LLMs are trained on vast amounts of data, much of which is sourced from the internet. However, this data is often biased towards certain perspectives, cultures, and demographics, which can result in LLMs producing biased and potentially harmful outputs.  

Another concern is the potential for LLMs to be used maliciously. Some malicious attacks that can be performed on LLMs include prompt injection, where an adversary can prompt the LLM to produce malicious content or override the original instructions and the employed filtering schemes. Adversarial prompting is another type of attack that involves using clever prompts to hijack the model output. Membership inference attacks, training data leakage, and malicious queries are other types of attacks that can be performed on LLMs. These attacks can result in the exposure of sensitive information, modification of the database, and unauthorized requests. 

Additionally, there is the question of transparency and hallucination. The complexity of LLMs and generative AI means that it can be difficult to understand how these models arrive at their outputs. Hallucination refers to the phenomenon where a language model generates output that is not grounded in reality or lacks coherence with the input provided. In other words, the model generates text that is not factually accurate or contextually relevant and does not correspond to any real-world experience. 

Hallucinations can occur in LLMs due to a variety of reasons such as insufficient training data, biased training data, and model architecture limitations. These hallucinations can range from minor errors in syntax and grammar to completely nonsensical and absurd output.  

Policy regulators in various countries are revisiting their stance over privacy concerns of LLMs. Italy is the first country to have banned ChatGPT earlier in April and lifted the ban towards the end of the month. Despite these concerns, we believe that there is a bright future for LLMs and generative AI. Stanford’s recent AI Index Report 2023 highlights that policymakers’ interest in AI is on the rise and that AI is both helping and harming the environment. Microsoft’s recent publication posits that developments from GPT-4 could be signs of Artificial General Intelligence (AGI), a paradigm that refers to the ability of machines to perform any intellectual task that a human can, something that researchers thought was decades away from being achieved. AGI includes tasks that are currently considered to be too complex for machines, such as creativity, empathy, and common sense.