3 ms·

That is correct, it's always hallucinating and making things up. Just because those hallucinations sometimes randomly happens to be right, people concluded tha

by Seb-C 3y ago

That is correct, it's always hallucinating and making things up. Just because those hallucinations sometimes randomly happens to be right, people concluded that being wrong is the exception, while being right is somehow the rule. It's like when people read [insert millenias old text here], finds a part that happens to illustrate something in their life today and conclude that it is a prophecy that predicted the future. The meaning/truth in those is nothing more than a cognitive bias from the mind of the reader, not an inherent quality of the text.

somewhereoutth 3y ago
For heavy LLM users, there is probably a dopamine hit when it does something right, much as gamblers get a hit when the fruit machine pays out. Perhaps LLM use is no more productive than gambling, and perhaps can be abused in a similar way.
- HKH2 3y ago
  You might not have found any uses that suit your niche, but that doesn't mean those of us who have are just making up stories about productivity.
  - somewhereoutth 3y ago
    "You might not have found any games that suit your skill set, but that doesn't mean those of us who have are just making up stories about making money" Sorry, somewhat trite and unfair, but, if there is a gambling-like dopamine reward cycle occurring, then the users would have a hard time being truly objective about any productivity boost in total. They may instead focus on the 'wins', without taking into account any overheads or 'losses', much as a gambler would do.
    - HKH2 3y ago
      Sure, confirmation bias exists, but you can compare with the alternatives. E.g. a search engine can give you zero useful results, and you can fine tune your query and still get nothing after scrolling through pages of results (Do people really take the losses into account when using search engines?) I find prompt engineering with LLMs more useful because you get nudged in interesting directions, and even if you come away with no direct results, you have more of an idea of what you are looking for. Maybe lateral thinking is overrated.
- intended 3y ago
  LLMs work very well if you know the domain you are using the LLM on. If you have the ability to verify the output is incorrect, you will gain productivity using LLMs.

by Seb-C 3y ago

somewhereoutth 3y ago

For heavy LLM users, there is probably a dopamine hit when it does something right, much as gamblers get a hit when the fruit machine pays out. Perhaps LLM use is no more productive than gambling, and perhaps can be abused in a similar way.

HKH2 3y ago
You might not have found any uses that suit your niche, but that doesn't mean those of us who have are just making up stories about productivity.
- somewhereoutth 3y ago
  "You might not have found any games that suit your skill set, but that doesn't mean those of us who have are just making up stories about making money" Sorry, somewhat trite and unfair, but, if there is a gambling-like dopamine reward cycle occurring, then the users would have a hard time being truly objective about any productivity boost in total. They may instead focus on the 'wins', without taking into account any overheads or 'losses', much as a gambler would do.
  - HKH2 3y ago
    Sure, confirmation bias exists, but you can compare with the alternatives. E.g. a search engine can give you zero useful results, and you can fine tune your query and still get nothing after scrolling through pages of results (Do people really take the losses into account when using search engines?) I find prompt engineering with LLMs more useful because you get nudged in interesting directions, and even if you come away with no direct results, you have more of an idea of what you are looking for. Maybe lateral thinking is overrated.
intended 3y ago
LLMs work very well if you know the domain you are using the LLM on. If you have the ability to verify the output is incorrect, you will gain productivity using LLMs.