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> Sure, and you can train LLMs to produce answers like that more often, but then users will say your model is lazy and doesn't even try, whereas if you train it

> Sure, and you can train LLMs to produce answers like that more often, but then users will say your model is lazy and doesn't even try, whereas if you train it to be more likely to produce something that looks like a solution more often, people will think “wow, the AI solved this problem I couldn't solve”. Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? Sure, there will be an error rate, but surely you can train an LLM to minimize it?

dragonwriter 3y ago
> Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? No, I am saying that they are specific trained to do that, and that the results seen in practice on common real-world LLMs reflect the bias of the specific training they are given for providing concrete answers. > Sure, there will be an error rate, but surely you can train an LLM to minimize it? Giving some answer to a question that cannot be infallibly solved analytically is not necessarily an error. In fact, I would argue that providing useful answers in situations like that is among the motivating use cases for AI. (Whether or not the answers current LLMs provide in these cases are useful is another question, but you miss 100% of the shots you don’t take.)
pixl97 3y ago
>Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? This is highly problematic and highly contextualized statement. Imagine you're an accountant with the piece of information $x. The answer you give for the statement "What is $x" is going to be highly dependent on who is answering the question. For example 1. The CEO asks "What is $x" 2. A regulator at the SEC asks "What is $x 3. Some random individual or member of the press asks "What is $x" An LLM doesn't have the other human motivations a person does when asked questions, pretty much at this point with LLMs there are only one or two 'voices' it hears (system prompt and user messages). Whereas a human will commonly lie and say I don't know, it's somewhat questionable if we want LLMs intentionally lying. In addition human information is quite often compartmentalized to keep secrets which is currently not in vogue with LLMs as we are attempting to make oracles that know everything with them.
- someplaceguy 3y ago
  > The answer you give for the statement "What is $x" is going to be highly dependent on who is answering the question. I assume you meant asking rather than answering? > An LLM doesn't have the other human motivations a person does when asked questions, pretty much at this point with LLMs there are only one or two 'voices' it hears (system prompt and user messages). Why would LLMs need any motivation besides how they are trained to be helpful and the given prompts? In my experience with ChatGPT 4, it seems to be pretty good at discerning what and how to answer based on the prompts and context alone. > Whereas a human will commonly lie and say I don't know, it's somewhat questionable if we want LLMs intentionally lying. Why did you jump to the conclusion that an LLM answering "I don't know" is lying? I want LLMs to answer "I don't know" when they don't have enough information to provide a true answer. That's not lying, in fact it's the opposite, because the alternative is to hallucinate an answer. Hallucinations are the "lies" in this scenario. > In addition human information is quite often compartmentalized to keep secrets which is currently not in vogue with LLMs as we are attempting to make oracles that know everything with them. I'd rather have an oracle that can discriminate when it doesn't have enough information to provide a true answer and replies "I don't know" in such cases (or sometimes answer like "If I were to guess, then bla bla bla, but I'm not sure about this"), than one which always gives confident but sometimes wrong answers.

dragonwriter 3y ago

> Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? No, I am saying that they are specific trained to do that, and that the results seen in practice on common real-world LLMs reflect the bias of the specific training they are given for providing concrete answers. > Sure, there will be an error rate, but surely you can train an LLM to minimize it? Giving some answer to a question that cannot be infallibly solved analytically is not necessarily an error. In fact, I would argue that providing useful answers in situations like that is among the motivating use cases for AI. (Whether or not the answers current LLMs provide in these cases are useful is another question, but you miss 100% of the shots you don’t take.)

pixl97 3y ago

>Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? This is highly problematic and highly contextualized statement. Imagine you're an accountant with the piece of information $x. The answer you give for the statement "What is $x" is going to be highly dependent on who is answering the question. For example 1. The CEO asks "What is $x" 2. A regulator at the SEC asks "What is $x 3. Some random individual or member of the press asks "What is $x" An LLM doesn't have the other human motivations a person does when asked questions, pretty much at this point with LLMs there are only one or two 'voices' it hears (system prompt and user messages). Whereas a human will commonly lie and say I don't know, it's somewhat questionable if we want LLMs intentionally lying. In addition human information is quite often compartmentalized to keep secrets which is currently not in vogue with LLMs as we are attempting to make oracles that know everything with them.

someplaceguy 3y ago
> The answer you give for the statement "What is $x" is going to be highly dependent on who is answering the question. I assume you meant asking rather than answering? > An LLM doesn't have the other human motivations a person does when asked questions, pretty much at this point with LLMs there are only one or two 'voices' it hears (system prompt and user messages). Why would LLMs need any motivation besides how they are trained to be helpful and the given prompts? In my experience with ChatGPT 4, it seems to be pretty good at discerning what and how to answer based on the prompts and context alone. > Whereas a human will commonly lie and say I don't know, it's somewhat questionable if we want LLMs intentionally lying. Why did you jump to the conclusion that an LLM answering "I don't know" is lying? I want LLMs to answer "I don't know" when they don't have enough information to provide a true answer. That's not lying, in fact it's the opposite, because the alternative is to hallucinate an answer. Hallucinations are the "lies" in this scenario. > In addition human information is quite often compartmentalized to keep secrets which is currently not in vogue with LLMs as we are attempting to make oracles that know everything with them. I'd rather have an oracle that can discriminate when it doesn't have enough information to provide a true answer and replies "I don't know" in such cases (or sometimes answer like "If I were to guess, then bla bla bla, but I'm not sure about this"), than one which always gives confident but sometimes wrong answers.