LLMs get it wrong…
When your favorite agent plays the Monday morning joke on you like a playful colleague…..and announces that you haven’t upgraded your PHP when it’s not true….
Little rascal.
LinkedIn Sources
- Profile: Pierre Jean Duvivier aka Sage (LinkedIn)
The post LLMs get it wrong… appeared first on les plumes qui mordent.
Frequently Asked Questions
FAQs When LLMs Get It Wrong
1 What does it mean when people say an LLM gets it wrong
It means the model gives an answer thats false misleading outdated or not what you asked foreven though it sounds confident
2 What is an LLM in simple terms
A Large Language Model is an AI trained on huge amounts of text to predict and generate language It doesnt know facts the way a person doesit produces likelysounding text
3 Why do LLMs make things up
Theyre built to generate plausible text not to verify truth When they lack the right information they can fill the gap with fluentsounding but false content
4 Whats a hallucination
A hallucination is when an LLM confidently states something that isnt truelike inventing a fake citation statistic or quote
5 Can LLMs give wrong answers even when they sound sure
Yes Confidence in tone says nothing about accuracy Fluent assertive answers can still be completely wrong
6 Why do LLMs sometimes get simple facts wrong
Their knowledge comes from training data which can be incomplete outdated or noisy They also dont look things up unless given a tool to do so
7 Do LLMs always give the same answer to the same question
No Many models are nondeterministic so wording and details can vary between runsand sometimes the accuracy varies too
8 Why does an LLM contradict itself in one conversation
It generates each response based on the conversation so far without a fixed memory of its earlier claims Long or complex chats make inconsistencies more likely
9 Why does it forget or mix up things I said earlier
Models have limited context windows and no persistent memory by default Details can get lost or blurred as the conversation grows
10 Why does the same question get different answers in different languages
Training data and performance vary by language so a model may be more accurate in English than in lessrepresented languages
11 What are common signs that an answer might be wrong
Overly specific numbers or citations with no source vague hedging mixed with confident claims answers that dodge your actual question or facts that seem too convenient
