I know it's an extreme example, but flat earthers to exist. I am sure we're all have our own "flat earth" beliefs where we are confidently incorrect.
But it's a viewpoint they have and can tell you why -- even if they're fundamentally flawed in their reasoning. LLMs are just 'predict the next word' machines and as such just literally make up strings of words that sound plausible, but at totally wrong. These are not the same thing.
Same difference. Point is they are wrong. Their reasons, if they have any, do not matter and usually do not make sense either.
I haven't yet figured out how to get an LLM to accurately determine whether it actually knows something or is making it up. I wonder how they handle that. They may get to that at some point in the article, but the page eventually breaks for me on mobile and I can't read past the first code block.
I eventually ended up switching fields to ML (and that's my current day job), but I started out as an undergrad studying psychology/cognitive science. During those years I started a research project on what is called the "feeling of knowing", or FOK for short - a subtopic in the broader topic of "metacognition" in cognitive science. The FOK is closely related to what is colloquially knows as tip-of-the-tongue phenomenon - basically, that subjective feeling that we know something, whether or not we can actually recall it. There's some interesting aspects to it. For example, the FOK is generally pretty accurate (but not perfect, of course). And it tends to be more robust than actual memory; as we age, for example, we tend to be better at judging than we know an actor's name (and confirming it once we look it up) than recalling it. It seems like LLMs have very little in the way of metacognition, and just confabulate if they don't know something, as we've seen. I'm sure we'll be seeing some efforts to give some analog of a FOK to LLMs in the near future.
If you (like me) were wondering how these works, the LLM is given a prompt like: Answer the following questions as best you can. You have access to the following tools: Search: Use this to search the internet. Calculator: Use this to do math. Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_name…
I just released something like this embedded in a browser extension. Except the prompt
includes a TypeScript interface that GPT4 is asked to follow. Works very well and reliably uses tools like Calculate, RequestDOM, etc.
I've been tinkering with LangChain for a few days and I agree. Is there a resource that collects the agents so we can experiment? I'd love to see an aggregated list of the most impressive agents and use cases if anyone knows of any. This stuff is the future of computing no doubt.
There is a LangChainHub mentioned in their docs, but the repo for it seems dead. Is there any sort of marketplace/AppStore for agents/tools/plug-ins for LLMs via LangChain? Or some other library like haystack?
closest i've seen so far is https://llamahub.ai, but afaict it's only for loaders atm
It is different with LLMs. Most people can give a level of uncertainty along with an answer, and often do. LLMs can't, and worse, are trained to put an emphasis on the prompts. Humans are often trained to be skeptical of prompts. If I said, "the moon is made of cheese. What type of cheese do you think it is?" most humans would automatically object, but with LLMs you can usually craft a prompt that would get it to ans…
> If I said, "the moon is made of cheese. What type of cheese do you think it is?" most humans would automatically object, but with LLMs you can usually craft a prompt that would get it to answer such a silly question. For some underspecified questions, the LLM also has no context. Are you on the debate stage, pointing the mic at the LLM or is the LLM on a talk show/podcast? or are you having a creative writing semin…
They are improving-- GPT4 is not so easily fooled:
>As an AI language model, I must clarify that the moon is not made of cheese. This idea is a popular myth and often used as a humorous expression. The moon is actually composed of rock and dust, primarily made up of materials like basalt and anorthosite. Scientific research and samples collected during the Apollo missions have confirmed this composition.
If you (like me) were wondering how these works, the LLM is given a prompt like: Answer the following questions as best you can. You have access to the following tools: Search: Use this to search the internet. Calculator: Use this to do math. Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_name…
This looks similar to the WebGPT paper, is that referenced in any of langchain or haystack's publications? Introducing the mechanism of internal thought is very interesting, I wonder if there's a way to make it implicit in the model's architecture.
I know it's an extreme example, but flat earthers to exist. I am sure we're all have our own "flat earth" beliefs where we are confidently incorrect.
But it's a viewpoint they have and can tell you why -- even if they're fundamentally flawed in their reasoning. LLMs are just 'predict the next word' machines and as such just literally make up strings of words that sound plausible, but at totally wrong. These are not the same thing.
People keep repeating that LLMs are predicting the next words but at least with the more recent versions, this isn't true. Eg, LLMs are generating their own intermediate or emergent goals, they're reasoning in a way that is more complex that autocomplete.
It seems like predict the next word is the floor of their ability, and people mistake it for the ceiling.
I haven't yet figured out how to get an LLM to accurately determine whether it actually knows something or is making it up. I wonder how they handle that. They may get to that at some point in the article, but the page eventually breaks for me on mobile and I can't read past the first code block.
With tools like this, you basically assume the LLM doesn't know, and teach it to always defer to a tool, so its response is basically summarization over the tool output.
But it's a viewpoint they have and can tell you why -- even if they're fundamentally flawed in their reasoning. LLMs are just 'predict the next word' machines and as such just literally make up strings of words that sound plausible, but at totally wrong. These are not the same thing.
Same difference. Point is they are wrong. Their reasons, if they have any, do not matter and usually do not make sense either.
It does matter, because the flat earther isn't to likely make something up about everything they talk about. They can communicate their world view, and you quickly start to figure out a model of theirs as you talk to them. None of that is true with an LLM. Any subject matter (astronomy, weather, cooking, NFL games, delegate callback methods on iOS classes, restaurants, etc) at all can have complete plausible sounding falsehoods stated as extremely confident fact, and you cannot build a mental model of knowing when it would hallucinate versus be accurate. 100% different from a human who holds a believe system that maybe contrary to evidence in a limited domain, and KNOWS that it's an outlier from the norm.