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Introducing Agents in Haystack: Make LLMs resolve complex tasks

haystack.deepset.ai

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Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#61

Post by azinman2 »

htss2013 wrote: post #58
azinman2 wrote: post #40

Earlier quoted context omitted.

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.

But ultimately it is predicting the next token. That's the taste. Using context from what's already been predicted, what comes before it, attention mechanisms to know how words relate, all of the intermediate embeddings and whatever they signify about the world -- that all just makes the next word prediction that much better.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#62

Post by oefnak »

azinman2 wrote: post #61
htss2013 wrote: post #58

Earlier quoted context omitted.

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.

But ultimately it is predicting the next token. That's the taste. Using context from what's already been predicted, what comes before it, attention mechanisms to know how words relate, all of the intermediate embeddings and whatever they signify about the world -- that all just makes the next word prediction that much better.

But intelligence *is* being able to make predictions! That's the entire reason we evolved intelligence! (Not words, but the world around us, sure, but apparently language makes a pretty good map)

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#63

Post by azinman2 »

oefnak wrote: post #62
azinman2 wrote: post #61

Earlier quoted context omitted.

But ultimately it is predicting the next token. That's the taste. Using context from what's already been predicted, what comes before it, attention mechanisms to know how words relate, all of the intermediate embeddings and whatever they signify about the world -- that all just makes the next word prediction that much better.

But intelligence *is* being able to make predictions! That's the entire reason we evolved intelligence! (Not words, but the world around us, sure, but apparently language makes a pretty good map)

Prediction is a faction of cognition. There’s a theory of self, perception, sensory fusion, incremental learning, emotions, a world model, communication and a sense of consequences, desire for self preservation and advancement, self-analysis and reflection, goal setting, reward-driven behavior, and so many more aspects that are missing from “predict the next word.”

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#65

Post by SanderNL »

azinman2 wrote: post #60
SanderNL wrote: post #51

Earlier quoted context omitted.

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…

Fair enough. Your point is valid and I hate to be that person, but..

> It does matter, because the flat earther isn't to likely make something up about everything they talk about.

I am less optimistic about this. It seems to me you are vastly overestimating the average person's rationality. Rational types are overwhelming minority. It always amazes me how even my own thin layer of rationality breaks down so very fast. I used to think we live on top of vast mountains of rationality, but now I feel more like we, deep down, are vast ancient Lovecraftian monsters with a thin layer of human veneer.

I'm not arguing that LLMs today are comparable to how humans can maintain a perspective and contain their own "hallucinations", but I am arguing that it is a matter of quantity, not quality. It's a matter of time (IMO).

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#66

Post by layer8 »

teruakohatu wrote: post #33
Spivak wrote: post #29

Earlier quoted context omitted.

I mean it kinda can. Here's the full prompt. I have no idea about aspartame, I just picked something that it's definitely not sure about. Answer with a JSON object of the form {"confidence": $ >, "en": $ >}. User: What is 2 + 2? Bot: {"confidence": "very", "en": "2 + 2 is 4"} User: Is aspartame healthy? Bot: {"confidence": "somewhat", "en": "Aspartame has not yet been found to have any adverse effects on humans."} Us…

Interesting. > The response: {"confidence": "very low", "en": "I'm not sure, but I don't think the moon is made of cheese."} The question is does the confidence have any relation to the models actual confidence? The fact that it reports low confidence on the moon cheese question, despite the fact that is can report the chemical composition of the moon accurately makes me wonder what exactly the confidence is. Seems m…

My view is that ChatGPT isn’t a singular “it”. Its output is a random sampling from a range of possible “its”, the only (soft) constraint being the contents of the current conversation.

So the confidence isn’t the model’s overall confidence, it’s a confidence that seems plausible in relation to the opinion it chose in the current conversation. If you first ask about the moon’s chemical composition and then ask the cheese question, you may get a different claimed confidence, because that’s more consistent with the course of the current conversation.

Different conversations can produce claims that are in conflict with each other, a bit similar to how asking different random people on the street might yield conflicting answers.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#67

Post by IIAOPSW »

teruakohatu wrote: post #27
wickoff wrote: post #19

Earlier quoted context omitted.

I have the same exact problem with people. Being inaccurate is a feature, not a bug.

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…

Usually, you get a lecture about how unethical it is to spread misinformation about the composition of the moon.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#68

Post by Nevermark »

azinman2 wrote: post #63
oefnak wrote: post #62

Earlier quoted context omitted.

But intelligence *is* being able to make predictions! That's the entire reason we evolved intelligence! (Not words, but the world around us, sure, but apparently language makes a pretty good map)

Prediction is a faction of cognition. There’s a theory of self, perception, sensory fusion, incremental learning, emotions, a world model, communication and a sense of consequences, desire for self preservation and advancement, self-analysis and reflection, goal setting, reward-driven behavior, and so many more aspects that are missing from “predict the next word.”

You are confusing the underlying algorithm, such as prediction improved by gradient optimization, with the algorithms that get learned based on that.

Such as all the functional relationships between concepts that end up being modeled, I.e. “understood” and applicable. Those complex relationships are what is learned in order to accomplish the prediction of complex phenomena, like real conversations & text. About every sort of concept or experience that people have.

Deep learning architectures don’t just capture associations, correlations, conditional probabilities, Markov chains, etc. They learn whatever functional relationships that are in the data.

(Technically, neural network style models are considered “universal approximators” and have the ability to model any function given enough parameters, data and computation.)

Your neurons and your mind/knowledge, have exactly the same relationship.

Simple learning algorithms can learn complex algorithms. Saying all they can do is the simple algorithm is very misleading.

It would be like saying logic circuits can only do logic. And’s, Or’s, Not’s. But not realizing that includes the ability to perform every possible algorithm.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#69

Post by broast »

Sol- wrote: post #57
sr-latch wrote: post #37

Earlier quoted context omitted.

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 think the ReAct paper also popularized this approach: https://arxiv.org/abs/2210.03629

Perhaps these papers are also just coincidence. This field is so new and this type of reasoned completion chaining seems like it was an inevitability. I imagine many other active gpt products that got started early hand rolled similar systems.

Re: Introducing Agents in Haystack: Make LLMs resolve complex tasks

#70

Post by ElFitz »

Ozzie_osman wrote: post #23

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 had some fun with a similar approach, but when generating large outputs, or retrieving large contexts, it can easily run into the context window limit.

I think this could be partially solved by intelligently summarising parts of the prompt history, while storing the original in some vector db, so the relevant parts can be retrieved at will.

Quite fun.

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