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> People who like to have a go at langchain seem to argue that this is “so trivial” you could just do it yourself… but also not flexible enough, so you should d

> People who like to have a go at langchain seem to argue that this is “so trivial” you could just do it yourself… but also not flexible enough, so you should do it yourself. At least for now and for the most popular usecases, this _is_ true. The framework seems as though it was written by people who had not actually done ML work prior to GPT4's announcement. Regardless if that's true or not; the whole point of a highly robust large language model is to be so robust that _every_ problem you have is easily defined as a formatted string. The whole idea of deep learning is you don't need rules engines and coded abstractions, just English or whatever other modality people are comfortable communicating with. This is not necessarily true for all such cases at the moment. RAG needs to do a semantic search before formatting the string, for instance. But as we go forward and models get even more robust and advanced, the need for any abstraction other than plain language goes to zero.

wokwokwok 3y ago
> the whole point of a highly robust large language model is to be so robust that _every_ problem you have is easily defined as a formatted string Using language models is about automation, parsing, etc. like any NLP task. What you’re talking about (it would be nice) is sufficiently distant to what we have right now as to be totally irrelevant. I agree langchain is a naive implementation, but NLP libraries are complicated. They have always been complicated. Not being complicated is not the goal of these libraries; it’s getting the job done.
- ShamelessC 3y ago
  > What you’re talking about (it would be nice) is sufficiently distant to what we have right now as to be totally irrelevant. I disagree. :shrug: Guess we'll see who is right in like 10-20 years. It also sounds as though we're talking about different things maybe? Because a lot of automation, parsing and NLP are very much "solved" tasks for GPT-4 ignoring (important) edge cases and respecting the relative lack of progress in the domain until GPT-3. If you need agents and stuff, then yeah we haven't got that figured out. But neither will you (general you) with your hodge podge of if statements wrapping an LLM.
  - krageon 3y ago
    If we can only see in 10-20 years, you are wrong today. Which is what the point was.

wokwokwok 3y ago

> the whole point of a highly robust large language model is to be so robust that _every_ problem you have is easily defined as a formatted string Using language models is about automation, parsing, etc. like any NLP task. What you’re talking about (it would be nice) is sufficiently distant to what we have right now as to be totally irrelevant. I agree langchain is a naive implementation, but NLP libraries are complicated. They have always been complicated. Not being complicated is not the goal of these libraries; it’s getting the job done.

ShamelessC 3y ago
> What you’re talking about (it would be nice) is sufficiently distant to what we have right now as to be totally irrelevant. I disagree. :shrug: Guess we'll see who is right in like 10-20 years. It also sounds as though we're talking about different things maybe? Because a lot of automation, parsing and NLP are very much "solved" tasks for GPT-4 ignoring (important) edge cases and respecting the relative lack of progress in the domain until GPT-3. If you need agents and stuff, then yeah we haven't got that figured out. But neither will you (general you) with your hodge podge of if statements wrapping an LLM.
- krageon 3y ago
  If we can only see in 10-20 years, you are wrong today. Which is what the point was.