Why is this question bad?

I see. Thanks for replying. I wonder if we move the topic to how your interest works, would that be your interest? 

Here is the full dialog, in case you are still interested.

• [Bob posts a problem about data classification] 
• Alice: You should use LLM. It especially suites your problem
• Bob: In my understanding LLM is only strong where the context is large. If the context is small then using regex gives better result? Also, regex has advantages of high accuracy, fast, understandable and debuggable?
• Alice: Not really. Also, regex cannot work with synonyms and it must be in form. LLM is trained on multiple data, so if you make good prompt then it's much better
• Bob: But the nature of catching synonyms is still depending on context. As the context is small then there is not much synonyms at the beginning. If even human cannot get them then how can machine recognize them?
• Alice: You should try it first. You are reasoning too much

There are some notes:

• By "regex" Bob actually means rule-based approach. He thought in the context of NLP people generally understand regex and rule-based approach as one
• He mistakes synonym with homonym. Had he been aware of that he might have not said "the nature of catching synonyms is still depending on context" 

These info are only revealed later on.

That said, I'm now shutting down this conversation :)   I'll read any further responses, but probably won't further engage.

Thanks for your patience. Your response is by far the most useful one I have. I really appreciate that.

If you find replying a short sentence isn't much burden, can you tell why you don't want to continue this conversation? Is it because continuing it doesn't stimulate your interest as you have on it in the beginning? Can you share why?

They could have a valid debate about budgets (both dev and runtime costs) and precision-recall tradeoffs, and whether the problem is simple enough that it's anywhere close to true that the rules will fit in someone's head in order to be testable and debuggable.

I'm not sure what "it's anywhere close to true that the rules will fit in someone's head in order to be testable and debuggable" means. I guess you just mean if the problem is simple enough then the rule-based approach is better. Is that correct?

I'll assuming yes here. While Bob indeed doesn't mention explicitly about budgets, I guess we can all assume that everyone wants budgets as low as possible. And since Bob thinks that since the context is small here, I think he thinks that the problem is simple enough, and if using the rule-based approach then the precision-recall tradeoffs won't bee applied here. Overall, I think that in his mind the rule-based approach is the perfect tool for the problem.

They can talk about what parts of the problem are best addressed by what tools.  But a general discussion of "if we have only one tool, should it be a hammer or a screwdriver" is probably unhelpful.

So while I really agree with this, I suppose the reason both sides stuck is because both assume that their tool has superiority over the other on the problem. Alice sees the problem and concludes that this must be a nail and can never be a screw, so obviously the screwdriver is bad. Bob sees the problem and concludes that this must be a screw and can never be a nail, so obviously the hammer is bad. 

So I guess instead of arguing whether the solution should be a screwdriver or a hammer, they should argue on whether the problem is a screw or a nail. Perhaps it's a combination of screw and nail. Bob does give more detail about the problem when he says "the context is small", and I guess in his mind it's a distinctive feature of a screw, but perhaps the reality is that it's a shared feature of both. He sees the shank and mistakes it with the helical thread.

I hope this make sense.

I see your point. I think it's time to tell the specifics. 

• [Bob posts a problem about data classification] 
• Alice: You should use LLM. It especially suites your problem
• Bob: In my understanding LLM is only strong where the context is large. If the context is small then using regex gives better result? Also, regex has advantages of high accuracy, fast, understandable and debuggable?
• Alice: Not really. Also, regex cannot work with synonyms and it must be in form. LLM is trained on multiple data, so if you make good prompt then it's much better
• Bob: But the nature of catching synonyms is still depending on context. As the context is small then there is not much synonyms at the beginning. If even human cannot get them then how can machine recognize them?
• Alice: You should try it first. You are reasoning too much

There are some notes:

• By "regex" Bob actually means rule-based approach. He thought in the context of NLP people generally understand regex and rule-based approach as one
• He mistakes synonym with homonym. Had he been aware of that he might have not said "the nature of catching synonyms is still depending on context" 

These info are only revealed later on. I understand that at the time of saying these are enough for Alice to conclude that Bob needs to learn more. And again, I understand that she doesn't want to waste time. But I think shutting down a conversation because of the experience state of the conversations partner is a sign of ad hominent. If she wants to save her time then she can just abandon the conversation. If she still want to give feedback while not having the burden to elaborate then she can tell Bob to read more about synonyms/context/LLM. Is that a correct thinking?

I guess it all comes down to this question: when Bob says T₁ is only strong in C, the nature of A is C, or C doesn't apply to the problem, why that is the evidence for him to haven't tried it, but not the evidence that he has actually tried it? You said that:

It is very unlikely that Bob has tried T1, since he gave a very weak theoretical argument in favour of using T2 instead, rather than a much stronger and practical argument "I tried T1, and here's why it didn't help".

But I don't see how saying "T₁ is only strong in C, the nature of A is C, C doesn't apply to the problem" is to argue how better T₂ is, but only about how T₁ doesn't help. Yes, the exact wording doesn't fit, but I interpret the meaning isn't much different. How is that different to "I tried T₁, and I realize that it only yields better results under condition C. But the problem doesn't have C to begin with, so it didn't help here"?

I can of course provide full detailed, but in the interest of keeping it as general as possible (or, to understand where further generalization is not possible), I want to get deeper on this before provide more specifics.

I agree that "you are reasoning badly from evidence we agree on" or "you need to both measure and reason a lot more clearly" are also reasonable interpretations of "you are reasoning too much". But isn't that the other sentence "You should try it first" makes the "you are empirically testing too little" interpretation more likely?

Can you simplify "idiosyncratic triggers of internal states"? Also, if most people are bad observers, then wouldn't that it's more helpful for them to have direct experience with it?

Do you mean that how the evidence is obtained in different problems and domains determines whether saying "You should try it first. You are reasoning too much" is still giving reasons on why T₁ is better? Can you elaborate or give examples?

Have they not agreed on the problem or on what would constitute a solution? 

I suppose it's because Bob isn't aware of all the things he needs to say before posting the question, and Alice assumes on what he needs while he thinks he doesn't need it.

should Bob have tried $T_1$, he would likely respond in another way

Why doesn't saying "T₁ is only strong in C, the nature of A is C" indicate that he has tried it?  

Yes, I haven't read the Sequences yet. Just the new user's guide. To make it quick, can you give me the section that's relevant to the question?

By "their perspectives", whose? If Alice's, then I think she would say all of them are incorrect. Because that's her position at the beginning. And that's useless information, since I don't know why she thinks so. If I know then I wouldn't ask this question at the beginning.