ChatGPT Replies:

1. Alignment: Aligning large language models (LLMs) with human values is challenging. The model learns from a broad dataset, typically without specific instructions about what is ‘good’ or ‘bad’. Misalignment can lead to outputs that are harmful or offensive, and it’s hard to perfectly align these models with any specific set of values.
2. Grounding: LLMs lack a true understanding of the world. They generate text based on patterns they’ve learned, not on a deep comprehension of the concepts involved. This lack of grounding can lead to responses that sound plausible but are nonsensical or factually incorrect.
3. Fairness: Bias in the training data can lead to bias in the model’s responses. If the training data contains sexist, racist, or other unfair biases, the model can learn and reproduce these biases, even unintentionally.
4. Auditability: It’s difficult to know exactly why a model made a specific prediction or decision. This lack of transparency (also known as the “black box” problem) can make it hard to audit the model’s decisions, particularly in sensitive areas like healthcare or finance.
5. Cost to create: Developing a state-of-the-art LLM requires significant computational resources, not to mention the time and expertise required to train and fine-tune the model. This can make it prohibitively expensive for smaller organizations or individual researchers.
6. Cost to operate: In addition to the cost of creation, LLMs require substantial resources to operate, particularly for models with billions of parameters. This can include both computational costs (like server time and electricity) and human costs (like monitoring and maintenance).
7. Hallucination: Sometimes, LLMs generate outputs that are completely fabricated or “hallucinated”. This can range from minor inaccuracies to entirely false statements, which can be problematic especially if the model is relied upon for accurate information.
8. Jailbreaking: Users might attempt to “jailbreak” the model, or manipulate it to generate harmful or inappropriate content. This could be done through carefully crafted inputs, or by trying to trick the model’s safeguards.
9. Guardrails: Implementing effective guardrails to prevent misuse is a challenge. While measures can be taken to avoid generating harmful content, these may also limit the model’s usefulness by preventing it from generating certain types of legitimate content.
10. Generalization: While LLMs are good at interpolating from the data they’ve seen, they can struggle with extrapolation, or making predictions about scenarios that are significantly different from their training data. This can lead to poor performance in novel situations, or when asked to generate content about recent events or advancements that occurred after their training cut-off.
11. Obsolete Data: An LLM is created on a certain date. It’s something like a magic box filled with billions of data points called parameters. GPT 2 had 1.7 billion, GPT 3 had 170 Billion and GPT 4 has an estimated 1 trillion. Each of these is a separate system and has to be created anew for each generation. If the creation date June 15, 2022, then the LLM is unaware of events that have happened after that date. This can create errors as its responses will not take into account events in the world or breakthroughs in science.