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No, I find it unwilling to produce factual information. For example Jeep consistently lands at the bottom of the reliability ratings. Try asking GPT if Jeeps

No, I find it unwilling to produce factual information. For example Jeep consistently lands at the bottom of the reliability ratings. Try asking GPT if Jeeps are reliable. The response reads like Jeep advertising.

afpx 2y ago
Yes, often unreliable. And, they will give different answers at different times. https://chatgpt.com/share/67f57459-2744-8009-a94e-3b67dce8fd1c https://chatgpt.com/share/67f57459-2744-8009-a94e-3b67dce8fd... “[Jeeps] often score below average in reliability rankings from sources like Consumer Reports and J.D. Power.”
- jay_kyburz 2y ago
  I tried the trick of asking Gemini to search the web. It respond by telling me Jeep was "average". When I checked the Consumer reports website it gave me, It scores Jeep 19th out of 22. I don't call that average.
  - llm_nerd 2y ago
    Which Gemini? I asked 2.5 Pro and this was the result- https://g.co/gemini/share/b5e5ea80548b https://g.co/gemini/share/b5e5ea80548b Seems entirely reasonable to me. Didn't have to trick it into providing citations.
    - jay_kyburz 2y ago
      Ahh, I'm only on 2.0 Flash.
      - afpx 2y ago
        Are you using AI studio?
- deleted 2y ago
  [deleted]
marcusverus 2y ago
LLMs are like humans. They don't know what you mean, only what you say. They can't tell you what you want to know, they can only answer the question you actually ask! The question you asked is broad and phrased in a way that begs a simplistic answer about the entire brand. Obviously an answer to that question will do a worse job of laying out the relative reliability of current jeep models than would a report which was created to address that specific question. If you want to know how modern Jeep models stack up against their peers in terms of reliability, try asking GPT that question!
- jay_kyburz 2y ago
  Actually, you hit the nail on the head, they are _not_ like humans because they only know what you say, not what you mean. A human would understand that when you ask a broad question, you want a broad answer. Broadly speaking, Jeeps are bad. Our current LLM are kneecapped because they are very reluctant to be negative.
s1artibartfast 2y ago
GPt models have a people-pleasing bias and positivity bias. If you want factual information, you have to modify your prompt. I imagine you would get very different results if you asked "are Jeeps more reliable than Toyotas", or "how do Jeeps compare to the median car in terms of reliability" My impression is that different llms are more or less people pleasing. I found grok is more willing to tell me something is a bad idea.
- hbn 2y ago
  I wouldn't be surprised if the training was weighted to favor text it learned from more "reliable" or "professional" resources, which in the case of products, would be official sales listings that talk about how great their product is.
  - jacobr1 2y ago
    It is more subtle than that. It is trained on "everything" so the specific adjectives and words you use in your prompt will cause it to generate very different responses. The fine tuning causes it to prefer by default more reliable/professional responses ... but that is not because the training data is weighted toward them as such. If you mention specific publications, or forums, it will give you responses more likely to come from those. Looking at the reasoning traces for for the new reasoning models you can actually see how fine tuning is moving toward having models list the assumptions around data sources, which should be trusted, list multiple perspectives and then summarize, resulting in better answers. You can do that today with non-reasoning models, but you need to prompt engineer it to ask for that explicitly. This process of identifying not just extant content, but teaching systems how to approach problem analysis (instruction tuning, reasoning traces, etc ...) will be key to influencing how the models work and increasingly how they are differentiated.
  - s1artibartfast 2y ago
    I don't think that's a big part of it, although it may be included. In general, the models lean towards being Yes-Men on just about every topic, including things without official sources. I think this is a byproduct of them being trained to be friendly and agreeable. Nobody wants a product that's rude or contrarian, and this puts a huge finger on the scale. I imagine an a model unfiltered for safety and attitude and political correctness would have less of this bias (but perhaps more of other biases)
0xbadcafebee 2y ago
Instead ask it to show you links to websites that review reliability ratings and highlight the results for Jeeps along with sources. It's annoying, but how you ask it questions is often more important than what you're asking. (This was a thing when search engines were first introduced too)

afpx 2y ago

Yes, often unreliable. And, they will give different answers at different times. https://chatgpt.com/share/67f57459-2744-8009-a94e-3b67dce8fd1c https://chatgpt.com/share/67f57459-2744-8009-a94e-3b67dce8fd... “[Jeeps] often score below average in reliability rankings from sources like Consumer Reports and J.D. Power.”

jay_kyburz 2y ago
I tried the trick of asking Gemini to search the web. It respond by telling me Jeep was "average". When I checked the Consumer reports website it gave me, It scores Jeep 19th out of 22. I don't call that average.
- llm_nerd 2y ago
  Which Gemini? I asked 2.5 Pro and this was the result- https://g.co/gemini/share/b5e5ea80548b https://g.co/gemini/share/b5e5ea80548b Seems entirely reasonable to me. Didn't have to trick it into providing citations.
  - jay_kyburz 2y ago
    Ahh, I'm only on 2.0 Flash.
    - afpx 2y ago
      Are you using AI studio?
deleted 2y ago
[deleted]

marcusverus 2y ago

LLMs are like humans. They don't know what you mean, only what you say. They can't tell you what you want to know, they can only answer the question you actually ask! The question you asked is broad and phrased in a way that begs a simplistic answer about the entire brand. Obviously an answer to that question will do a worse job of laying out the relative reliability of current jeep models than would a report which was created to address that specific question. If you want to know how modern Jeep models stack up against their peers in terms of reliability, try asking GPT that question!

jay_kyburz 2y ago
Actually, you hit the nail on the head, they are _not_ like humans because they only know what you say, not what you mean. A human would understand that when you ask a broad question, you want a broad answer. Broadly speaking, Jeeps are bad. Our current LLM are kneecapped because they are very reluctant to be negative.

s1artibartfast 2y ago

GPt models have a people-pleasing bias and positivity bias. If you want factual information, you have to modify your prompt. I imagine you would get very different results if you asked "are Jeeps more reliable than Toyotas", or "how do Jeeps compare to the median car in terms of reliability" My impression is that different llms are more or less people pleasing. I found grok is more willing to tell me something is a bad idea.

hbn 2y ago
I wouldn't be surprised if the training was weighted to favor text it learned from more "reliable" or "professional" resources, which in the case of products, would be official sales listings that talk about how great their product is.
- jacobr1 2y ago
  It is more subtle than that. It is trained on "everything" so the specific adjectives and words you use in your prompt will cause it to generate very different responses. The fine tuning causes it to prefer by default more reliable/professional responses ... but that is not because the training data is weighted toward them as such. If you mention specific publications, or forums, it will give you responses more likely to come from those. Looking at the reasoning traces for for the new reasoning models you can actually see how fine tuning is moving toward having models list the assumptions around data sources, which should be trusted, list multiple perspectives and then summarize, resulting in better answers. You can do that today with non-reasoning models, but you need to prompt engineer it to ask for that explicitly. This process of identifying not just extant content, but teaching systems how to approach problem analysis (instruction tuning, reasoning traces, etc ...) will be key to influencing how the models work and increasingly how they are differentiated.
- s1artibartfast 2y ago
  I don't think that's a big part of it, although it may be included. In general, the models lean towards being Yes-Men on just about every topic, including things without official sources. I think this is a byproduct of them being trained to be friendly and agreeable. Nobody wants a product that's rude or contrarian, and this puts a huge finger on the scale. I imagine an a model unfiltered for safety and attitude and political correctness would have less of this bias (but perhaps more of other biases)

0xbadcafebee 2y ago

Instead ask it to show you links to websites that review reliability ratings and highlight the results for Jeeps along with sources. It's annoying, but how you ask it questions is often more important than what you're asking. (This was a thing when search engines were first introduced too)