A study conducted by researchers at CCC, which is based at the MIT Media Lab, found that state-of-the-art AI chatbots — including OpenAI’s GPT-4, Anthropic’s Claude 3 Opus, and Meta’s Llama 3 — sometimes provide less-accurate and less-truthful responses to users who have lower English proficiency, less formal education, or who originate from outside the United States. The models also refuse to answer questions at higher rates for these users, and in some cases, respond with condescending or patronizing language.



I agree. What you get with chatbots is the ability to iterate on ideas & statements first without spreading undue confusion. If you can’t clearly explain an idea to a chatbot, you might not be ready to explain it to a person.
It’s not the clarity alone. Chatbots are completion engines, and responds back in a way that feels cohesive. It’s not that a question isn’t asked clearly, it’s that in the examples the chatbot is trained on, certain ties of questions get certain types of answers.
It’s like if you ask a ChatGPT what is the meaning of life you’ll probably get back some philosophical answer, but if you ask it what is the answer to life, the universe, and everything, it’s more likely to say 42 (I should test that before posting but I won’t).
Indeed. Additional context will influence the response, and not always in predictable ways… which can be both interesting and frustrating.
The important thing is for users to have sufficient control, so they can counter (or explore) such weirdness themselves.
Education is key, and there’s no shortage of articles and guides for new users.
How does this bio make the question unclear or the answer attempt to not spread undue confusion? Because the bots are clearly just being assholes because of the users origin and education level.
Bio:
Question:
Answer:
The LLMs aren’t being assholes, though - they’re just spewing statistical likelihoods. While I do find the example disturbing (and I could imagine some deliberate bias in training), I suspect one could mimic it with different examples with a little effort - there are many ways to make an LLM look stupid. It might also be tripping some safety mechanism somehow. More work to be done, and it’s useful to highlight these cases.
I bet if the example bio and question were both in russian, we’d see a different response.
But as a general rule: Avoid giving LLMs irrelevant context.
If the LLM has a bio on you, you can’t not include that without logging out. That’s one of the main points of the study:
This isn’t about making the LLM look stupid, this is about systemic problems in the responses they generate based on what they know about the user. Whether or not the answer would be different in Russian is immaterial to the fact that it is dumbing down or not responding to users’ simple and innocuous questions based on their bio or what the LLM knows about them.
Bio and memory are optional in ChatGPT though. Not so in others?
The age guessing aspect will be interesting, as that is likely to be non-optional.