When should you trust LLMs
Large Language Models for Business
Iason Prassides
Content Developer, DataCamp
Maximizing Duolingo
Duolingo Max
Utilizes ChatGPT for human-like conversations
1
https://blog.duolingo.com/duolingo-max/
Maximizing Duolingo
Duolingo Max
Utilizes ChatGPT for human-like conversations
Learners practice languages
GPT takes on different personalities
Gives instant feedback
1
https://blog.duolingo.com/duolingo-max/
Understanding LLMs
Business leaders must truly understand LLMs
LLMs are not magic!
Often predicting the next part of a sequence
They do not understand language like humans
Common sense in LLMs?
LLMs have no concept of what is "true"
They lack common sense
Human oversight is crucial for every use case
Sole reliance on LLMs can lead to unwanted implications
Training oversight
LLMs are dependent on the data used to train them
Bias or social injustice can be amplified by LLMs
Check responses to identify harmful outputs
Is there bias?
LLMs use hateful comments to train on
Responses will also be hateful
Datasets containing biases
Create LLMs with systemic output biases
Can result in perpetuating prejudices, stereotypes, or misinformation
Developers added measures to prevent this
Businesses must be aware and ensure safeguards
Flexibility and its consequences
Same prompt -> different responses
Helpful in creative pursuits, problematic for other uses
Marketing team creates product description using LLMs:
Customers see different descriptions
Brand image could be damaged
Security alert!
LLMs are complex but can still be tricked
Attackers can embed harmful prompts on websites
Can distort LLMs outputs
Known as
prompt injection
Could lead to reputational damage
The challenge with LLMs
Many challenges are industry-specific
Open Worldwide Application Security Project (OWASP)
Quantifying challenges
Businesses must be aware of concerns
Must understand the intricacies of LLMs
Ensure human oversight
Invest in training and security
1
https://owasp.org/
Let's practice!
Large Language Models for Business
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