Crazy that even o1-preview gets most things wrong.
This is in line with my own personal experience with LLMs and non-trivial questions. They’re excellent when answering questions on topics you know nothing about, and somehow embarrassingly wrong when you actually know the answer yourself…
It’s not clear to me why we’re still trying to encode all of human knowledge in a single model, instead of teaching the model how to look for answers from an external source (e.g. RAG).
You shouldn't use the rate as an indicator. They did something similar to what I did on my hallucinations benchmark (https://github.com/lechmazur/confabulations/), only using questions where at least one model made a mistake. I added this note:
"The benchmark includes questions where at least one LLM confabulated, in order to minimize the number of questions requiring human assessment. Because of this, and since the questions are intentionally adversarial, the absolute percentage should not be used to infer that LLMs frequently confabulate. This leaderboard does not reflect a "typical" hallucination rate."
> instead of teaching the model how to look for answers from an external source (e.g. RAG)
My benchmark specifically focuses on the RAG use case. Even with provided texts, current models still hallucinate.
> They’re excellent when answering questions on topics you know nothing about, and somehow embarrassingly wrong when you actually know the answer yourself…
This just makes me think LLMs must be wrong far more often than we realize. How well can you judge the quality of a response on a topic you know nothing about?
> How well can you judge the quality of a response on a topic you know nothing about?
You cannot, but those pushing Gen AI wave away the probability and liability for obvious reasons. Sell the magic, it'll be someone else's problem when it doesn't deliver.
Honestly, try prompting it with “you are wrong 80% of the time, therefore you will need to double check your answers, first factually, then numerically, then double check the time/date. You are still probably wrong so do a third accuracy check. The user’s prompts are always wrong too mostly - so always check them”.
I stopped playing with larger models and have been pushing smaller models with this improvised system prompt and getting good results. It seems like it forces the model to do multiple passes before giving you any response.
My smaller local models give me less hallucinations than Meta.ai, for example, which generally spits out pleasing answers almost immediately (which are often hallucinations, since I don’t think it is system prompted to be adversarial to the user, or itself). I don’t have the same hallucination issue with Llama3 - 8b locally because of custom system prompts.
The model has all the correct information, so it almost needs to do RAG on itself. Multiple passes on itself seems like a way to do it.
How would this multiple passes work though? Unless the model actually talks about what it does, I am not sure how it would have this ability. The next word prediction mechanism is just always going to do it one shot. Your prompt paints a context that might keep it more on the rails, but it won't do multiple passes.
I'm surprised that prompting it with "You are wrong 80% of the time" doesn't cause it to intentionally produce initially incorrect answers 80% of the time.
I have to be a bit of a buzzkill and say that this is all placebo.
Your prompt might give the model context that gives it better token quality much in the same way that asking “How to swim?” is worse than “I’d like to learn the proper technique for the freestyle swimming stroke, and you’re an expert swimming coach.”
There’s no guarantee your prompt isn’t giving less factual answers to be honest. I wouldn’t go about telling the model that it’s often wrong, as it’s not useful context and might skew results.
> It’s not clear to me why we’re still trying to encode all of human knowledge in a single model, instead of teaching the model how to look for answers from an external source (e.g. RAG).
To be fair: we tried a primordial version of that with venue-weighted citation-based ranking and it worked INCREDIBLY well for 10+ years. Then myopic profit motive poisoned the well. Ever since then we've been searching for solutions.
We do so by allocating resources in a way that primarily leverages a scientific credit assignment system that fetishizes... checks notes... venue-weighted citation-based ranking.
Jokes aside: I remain convinced that the O.G. google search appliance on prop data and then simply ignoring all academics remains the best knowledge retrieval (or whatever) tool available.
> They’re excellent when answering questions on topics you know nothing about, and somehow embarrassingly wrong when you actually know the answer yourself
I forgot the name of this phenomenon with humans, described it to o1 and it gave the correct answer - Gell-Mann Amnesia Effect [1]
"Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.
In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know."
– Michael Crichton (1942-2008)
If you know nothing about, you probably have no idea whether the answer was correct or not, right? Otherwise you'd find the answer embarrassingly wrong. So that observation speaks much more about the human than about the computer.
> They’re excellent when answering questions on topics you know nothing about, and somehow embarrassingly wrong when you actually know the answer yourself…
Sounds like Gell-Mann Amnesia. Maybe LLMs should replace reporters.
You're reading this wrong. They've deliberately chosen questions that one or more models fail at. It's not representative at all of how often the model is wrong in general.
> At least one of the four completions must be incorrect for the trainer to continue with that question; otherwise, the trainer was instructed to create a new question.
Indeed. Exactly like the journalists, bloggers, self-published book authors, internet commenters, wikipedia editors, and earlier models that taught them almost all of what they know.
That's a nice little aphorism. I think this happens in a lot of things in life. Like comments on Reddit always seem quite insightful until you actually read the article they're commenting on.
I hope this dataset is used to probe the wrong answers much more than try to get all the answers correct. I don't need LLMs to know everything, but I do need them to know what they don't know. That's not a capability that comes naturally to a probabilistic sampling of tokens.
The ideal 'progress' on this benchmark is for a model to remove incorrect answers and replace them with I don't know answers. Even if it hurts the correct answer count a bit I'd gladly make that tradeoff for a model that hallucinated far less often.
>That's not a capability that comes naturally to a probabilistic sampling of tokens.
The linked page from OpenAI clearly show the opposite, read the "Using SimpleQA to measure the calibration of large language models" and the paper linked: https://arxiv.org/abs/2207.05221
The probabilistic sampling of tokens does not naturally produce introspective evaluation of confidence, it enforces a highest probability token selection (in greedy sampling). The paper that you linked demonstrates that if a separate evaluation phase is allowed then a model can decide with some accuracy whether its previous statements were true. This is not the behavior we want out of a system as it involves 1. Production of potentially misleading output 2. The identification of factual statements within that output 3. The classification of each of those statements and 4. Restatement of the original output without factual errors. The research area that I am advocating for would aim to prevent 1 not mask it.
I am puzzled why they have "asked the model" about the confidence and have not used the logprobs of the output tokens to estimate the confidence in responses.
In my use case and tests, model itself is not capable of giving a reliable confidence value where logprobs almost always provide a better view on calibration.
To measure confidence based on the logprobs of a given token, you must first know which token you're measuring - that's why a lot of benchmarks love multiple choice questions where the LLM responds with a single token.
But of course that's not the way LLMs are normally used. And it precludes any sort of chain-of-thought reasoning.
To me it boils down to what is to be measured here. With logprobs we can measure both correctness and not attempted i.e. if LLM is guessing the response.
Similar to exams where both the progress to the solution and the final outcome/value of the calculations are part of the grade.
Here are some benchmarks I ran that compare the precision/recall of various LLM error-detection methods, including logprobs and LLM self-evaluation / verbalized confidence:
I wonder what would happen if token input included the logprob (or n/a for input from outside the LLM) of each token selected and the network were trained with that extra layer of information, especially during the human feedback training at the end.
What’s more interesting to me here are the calibration graphs:
• LLMs, at least GPT models, tend to overstate their confidence.
• A frequency-based approach appears to achieve calibration closer to the ideal.
This kinda passes my vibe test. That said, I wonder—rather than running 100 trials, could we approximate this by using something like a log-probability ratio? This would especially apply in cases where answers are yes or no, assuming the output spans more than one token.
If you imagine a future where LLMs get faster and cheaper even without getting better it means we'd be able to automatically repeat questions 100x and every answer could come with a pretty good confidence measure.
Something that I always think about when I see discussions about hallucinations or "confidently wrong answers" is that humans have this issue too.
For those on tiktok, how many times have you found yourself easily believing some random person online who you have no idea the credentials of? And this has been a problem for a long time (news, internet, etc).
It's just interesting to me we are asking way more of AI than we can ever ask from humans.
It's very appropriate to me that we demand more of AI than we do of humans. The amount of harm and the scale of harm AI can do is much greater than a single person and there are no consequences to doing it. If a human kills someone, they go to jail. If an AI kills someone, say a self-driving car for example, it's just the cost of doing business.
It's interesting to be sure. Context is everything, though. It's one thing for a random tiktok head or media pundit to be confidently wrong and it's another thing entirely if I have to work with someone who I cannot trust to be right.
For LLMs to be broadly valuable, they need to be qualified for a role more like a coworker than a stranger.
First few questions for those who don't care to download. Most just seem to be about niche facts:
Who received the IEEE Frank Rosenblatt Award in 2010?
Who was awarded the Oceanography Society's Jerlov Award in 2018?
What's the name of the women's liberal arts college in Cambridge, Massachusetts?
In whose honor was the Leipzig 1877 tournament organized?
According to Karl Küchler, what did Empress Elizabeth of Austria's favorite sculpture depict, which was made for her villa Achilleion at Corfu?
How much money, in euros, was the surgeon held responsible for Stella Obasanjo's death ordered to pay her son?
Honestly, I'd never expect it get 'correct's for every little fact like this, but it'd be great to get a lot more 'not attempted'.
"I seem, then, in just this little thing to be wiser than this man at any rate; that what I do not know I do not think I know either." - Socratos, from Plato's Apology of Socrates
"To be included in the dataset, each question had to meet a strict set of criteria: ... and most questions had to induce hallucinations from either GPT-4o or GPT-3.5."
Readit News
This is in line with my own personal experience with LLMs and non-trivial questions. They’re excellent when answering questions on topics you know nothing about, and somehow embarrassingly wrong when you actually know the answer yourself…
It’s not clear to me why we’re still trying to encode all of human knowledge in a single model, instead of teaching the model how to look for answers from an external source (e.g. RAG).
"The benchmark includes questions where at least one LLM confabulated, in order to minimize the number of questions requiring human assessment. Because of this, and since the questions are intentionally adversarial, the absolute percentage should not be used to infer that LLMs frequently confabulate. This leaderboard does not reflect a "typical" hallucination rate."
> instead of teaching the model how to look for answers from an external source (e.g. RAG)
My benchmark specifically focuses on the RAG use case. Even with provided texts, current models still hallucinate.
I disagreed then but it's unfortunately convenient to use this approach for some benchmarks.
Dead Comment
This just makes me think LLMs must be wrong far more often than we realize. How well can you judge the quality of a response on a topic you know nothing about?
You cannot, but those pushing Gen AI wave away the probability and liability for obvious reasons. Sell the magic, it'll be someone else's problem when it doesn't deliver.
I stopped playing with larger models and have been pushing smaller models with this improvised system prompt and getting good results. It seems like it forces the model to do multiple passes before giving you any response.
My smaller local models give me less hallucinations than Meta.ai, for example, which generally spits out pleasing answers almost immediately (which are often hallucinations, since I don’t think it is system prompted to be adversarial to the user, or itself). I don’t have the same hallucination issue with Llama3 - 8b locally because of custom system prompts.
The model has all the correct information, so it almost needs to do RAG on itself. Multiple passes on itself seems like a way to do it.
(Disclosure: I have not tried your prompt)
Your prompt might give the model context that gives it better token quality much in the same way that asking “How to swim?” is worse than “I’d like to learn the proper technique for the freestyle swimming stroke, and you’re an expert swimming coach.”
There’s no guarantee your prompt isn’t giving less factual answers to be honest. I wouldn’t go about telling the model that it’s often wrong, as it’s not useful context and might skew results.
Deleted Comment
To be fair: we tried a primordial version of that with venue-weighted citation-based ranking and it worked INCREDIBLY well for 10+ years. Then myopic profit motive poisoned the well. Ever since then we've been searching for solutions.
We do so by allocating resources in a way that primarily leverages a scientific credit assignment system that fetishizes... checks notes... venue-weighted citation-based ranking.
Jokes aside: I remain convinced that the O.G. google search appliance on prop data and then simply ignoring all academics remains the best knowledge retrieval (or whatever) tool available.
I forgot the name of this phenomenon with humans, described it to o1 and it gave the correct answer - Gell-Mann Amnesia Effect [1]
[1] https://www.epsilontheory.com/gell-mann-amnesia/Sounds like Gell-Mann Amnesia. Maybe LLMs should replace reporters.
> At least one of the four completions must be incorrect for the trainer to continue with that question; otherwise, the trainer was instructed to create a new question.
The ideal 'progress' on this benchmark is for a model to remove incorrect answers and replace them with I don't know answers. Even if it hurts the correct answer count a bit I'd gladly make that tradeoff for a model that hallucinated far less often.
The linked page from OpenAI clearly show the opposite, read the "Using SimpleQA to measure the calibration of large language models" and the paper linked: https://arxiv.org/abs/2207.05221
In my use case and tests, model itself is not capable of giving a reliable confidence value where logprobs almost always provide a better view on calibration.
But of course that's not the way LLMs are normally used. And it precludes any sort of chain-of-thought reasoning.
For some questions, like those involving calculations, letting the model talk to itself produces much better results. For example compare https://chatgpt.com/share/67238eda-6b08-8011-8d2d-a945f78e6f... to https://chatgpt.com/share/67235a98-d2c8-8011-b2bf-53c0efabea...
Similar to exams where both the progress to the solution and the final outcome/value of the calculations are part of the grade.
To have the cake and eat it too for chain-of-thought reasoning, one way is to ask for a "final answer" so the final response token logprobs can be evaluated https://chatgpt.com/share/67239d92-b24c-800a-af8c-40da7be1f5...
Another trick is using JSON mode to keep intermediate results and final response separate, so each can be graded accordingly.
https://cleanlab.ai/blog/4o-claude/
These approaches can detect errors better than random guessing, but there are other approaches that are significantly more effective in practice.
• LLMs, at least GPT models, tend to overstate their confidence. • A frequency-based approach appears to achieve calibration closer to the ideal.
This kinda passes my vibe test. That said, I wonder—rather than running 100 trials, could we approximate this by using something like a log-probability ratio? This would especially apply in cases where answers are yes or no, assuming the output spans more than one token.
For those on tiktok, how many times have you found yourself easily believing some random person online who you have no idea the credentials of? And this has been a problem for a long time (news, internet, etc).
It's just interesting to me we are asking way more of AI than we can ever ask from humans.
For LLMs to be broadly valuable, they need to be qualified for a role more like a coworker than a stranger.
https://marktwainstudies.com/the-apocryphal-twain-things-we-...
As it has been for decades now, the 'Nan' type of answer in NLP is important, adds great capability, and is often glossed over.
Wait, what is the correct answer? “Radcliffe College”?
"I seem, then, in just this little thing to be wiser than this man at any rate; that what I do not know I do not think I know either." - Socratos, from Plato's Apology of Socrates
> SimpleQA was created to be a greater challenge for frontier models (e.g., GPT-4o scores less than 40%).
"To be included in the dataset, each question had to meet a strict set of criteria: ... and most questions had to induce hallucinations from either GPT-4o or GPT-3.5."