"For instance, let's say you want to trigger some alert when a particular kind of article is published or a particular kind of message is sent. Triggering the alert is a boolean thing, so that has to be a classification task."
I think this is a good example of how decomposed tasks can feel very different from goal-oriented task definitions.
We can imagine a goal like "I want to know when one of my competitors shows up in the news." Now you have a bunch of tasks: entity extraction, determining the subject of an article, maybe categorizing articles. Then you can define a pipeline and conditional to trigger a notification. And you might get great accuracy on each of these.
But the goal is really about getting actionable information. In practice the approach above creates a ton of alerts, and the person receiving them will filter through them, ignore a bunch, have to figure out what is really new, etc. An LLM could do things like accumulate a running set of background knowledge, identifying what information is truly "new" (and in a granular way, not just detecting duplicate articles). You can tell the LLM all kinds of details about what you are interested in, "categories" that are completely inaccessible to traditional NLP because they are described by higher-level concepts or have to be combined with history or user-provided context (something that happens naturally in a prompt).
Traditional NLP feels very industrial to me. Factories can be very productive and high volume, but they redefine the tasks to satisfy their processes. Individuals don't interface with factories, and factories don't serve individuals.
I think the 'industrial' or assembly-line analogy is probably good, and I see what you mean about the alternative system design. Thanks for explaining the other approach.
I'll put it this way. If you want to integrate ML into a product, or even a system with lots of internal users, you end up increasingly towards the 'factory' approach.
I think this is a good example of how decomposed tasks can feel very different from goal-oriented task definitions.
We can imagine a goal like "I want to know when one of my competitors shows up in the news." Now you have a bunch of tasks: entity extraction, determining the subject of an article, maybe categorizing articles. Then you can define a pipeline and conditional to trigger a notification. And you might get great accuracy on each of these.
But the goal is really about getting actionable information. In practice the approach above creates a ton of alerts, and the person receiving them will filter through them, ignore a bunch, have to figure out what is really new, etc. An LLM could do things like accumulate a running set of background knowledge, identifying what information is truly "new" (and in a granular way, not just detecting duplicate articles). You can tell the LLM all kinds of details about what you are interested in, "categories" that are completely inaccessible to traditional NLP because they are described by higher-level concepts or have to be combined with history or user-provided context (something that happens naturally in a prompt).
Traditional NLP feels very industrial to me. Factories can be very productive and high volume, but they redefine the tasks to satisfy their processes. Individuals don't interface with factories, and factories don't serve individuals.