While GenAI and LLMs provide a lot of potential, you should also be cautious.
At their core, LLMs are highly complex predictive text machines. LLM’s don’t know or understand the information they output; they simply predict the next word in a sequence.
The words are based on the patterns and relationships from other text in the training data.
Access to Data
The sources for this training data are often the internet, books, and other publicly available text. The data could be of questionable quality and maybe be incorrect.
Training happens at a point in time, it may not reflect the current state of the world and would not include any private information.
When asked to provide a response, relating to new or data not in the training set, the LLM may provide a response that is not accurate.
Accuracy
LLMs are designed to create human-like text and are often fine-tuned to be as helpful as possible, even if that means occasionally generating misleading or baseless content, a phenomenon known as hallucination.
For example, when asked to "Describe the moon." an LLM may respond with "The moon is made of cheese.". While this is a common saying, it is not true.
While LLMs can represent the essence of words and phrases, they don’t possess a genuine understanding or ethical judgment of the content.
Temperature
LLMs have a temperature, corresponding to the amount of randomness the underlying model should use when generating the text.
The higher the temperature value, the more random the generated result will become, and the more likely the response will contain false statements.
A higher temperature may be appropriate when configuring an LLM to respond with more diverse and creative outputs, but it comes at the expense of consistency and precision.
For example, a higher temperature may be suitable for constructing a work of fiction or a novel joke, a lower temperature is required when a response grounded in facts is essential.
Consider the correct temperature
In June 2023, A US judged sanctioned two US lawyers for submitting an LLM-generated legal brief that contained six fictitious case citations.
A quick fix may be to reduce the temperature. But more likely, the LLM is hallucinating because it hasn’t got the information required.
Transparency
GenAI Models are often considered "black boxes" due to the difficulty deciphering their decision-making processes. The LLM would also be unable to provide the sources for its output or explain its reasoning.
All these factors can lead to outputs that might be biased, devoid of context, or lack logical coherence.
Check Your Understanding
False Negatives
What is the name given to a confident, but incorrect answer provided by an LLM?
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❏ Day Dream
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✓ Hallucination
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❏ Illusion
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❏ Ungrounding
Hint
This phenomenon can occur when the LLM is unaware of the concept, either through bad data or a cut-off date in the training data.
Solution
The answer is Hallucination.
Lesson Summary
In this lesson, you learned about the limitations of GenAI models, including hallucination, and access to data.
In the next lesson, you will learn about how to provide context to GenAI models to improve the accuracy of their responses.