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I'm sure it's going to get exponentially better, but right now ChatGPT sounds like one very vanilla guy. The other day I asked it to write me a conversation be

by 13of40 4y ago

I'm sure it's going to get exponentially better, but right now ChatGPT sounds like one very vanilla guy. The other day I asked it to write me a conversation between David Foster Wallace and Ernest Hemingway, and it came out something like (paraphrasing): HEMINGWAY: I notice you write about political issues and philosophy. WALLACE: Thank you, I noticed you write about raw experience and manliness. HEMINGWAY: Well, whatever our differences I feel like we can get along and work together. We will soon be fast friends.

chatmasta 4y ago
I tried to get it to intentionally respond with grammatically incorrect sentences and it had a lot of trouble. I basically couldn't get it to work.
Nition 4y ago
Has ChatGPT actually read any books (apart from snippets on the Internet), or has that been avoided for copyright reasons? I wonder how long until someone pirates every ebook on Z-Library or SciHub and trains a language model off that. Imagine a language model that's read every book and every scientific paper.
- techdragon 4y ago
  I wonder if that would even generate good outcomes given how much of the data in some disciplines is conflicting, and if you don’t have a good way to get it to ignore bad subsets of that data it could be more likely to hallucinate convincing scientific sounding explanations based on a preponderance of bad poorly researched evidence. There’s a lot of papers that discredit older papers or work or entire subfields of study and the current large language models would just all all of it and go “these are words” with no analytical reasoning applied. These are predictive mechanisms not analytical ones and until we get a better handle on how we can make them “stop and think”… I’m just not sure how much benefit each larger dataset will add beyond “sounds more human” while it’s core problems of “still hallucinates” and “can’t do basic reasoning” remain. Also one question I’ve got on this front is how much duplicate data is processed out of the input sets? Using LibGen as an example there’s a lot of books that get reprinted and uploaded multiple times in different formats… does having these remain in the data provide a “valuable bias” or is it something that needs removing? Do the people building large language models pre-process their data to de-duplicate out these kinds of things from their current data sources?
bradleyjg 4y ago
I asked it to make a certain stand up bit in the style of various comedians. The Seinfeld one was reasonably good but the Chris Rock one was off because it wouldn’t curse.

by 13of40 4y ago

chatmasta 4y ago

I tried to get it to intentionally respond with grammatically incorrect sentences and it had a lot of trouble. I basically couldn't get it to work.

Nition 4y ago

Has ChatGPT actually read any books (apart from snippets on the Internet), or has that been avoided for copyright reasons? I wonder how long until someone pirates every ebook on Z-Library or SciHub and trains a language model off that. Imagine a language model that's read every book and every scientific paper.

techdragon 4y ago
I wonder if that would even generate good outcomes given how much of the data in some disciplines is conflicting, and if you don’t have a good way to get it to ignore bad subsets of that data it could be more likely to hallucinate convincing scientific sounding explanations based on a preponderance of bad poorly researched evidence. There’s a lot of papers that discredit older papers or work or entire subfields of study and the current large language models would just all all of it and go “these are words” with no analytical reasoning applied. These are predictive mechanisms not analytical ones and until we get a better handle on how we can make them “stop and think”… I’m just not sure how much benefit each larger dataset will add beyond “sounds more human” while it’s core problems of “still hallucinates” and “can’t do basic reasoning” remain. Also one question I’ve got on this front is how much duplicate data is processed out of the input sets? Using LibGen as an example there’s a lot of books that get reprinted and uploaded multiple times in different formats… does having these remain in the data provide a “valuable bias” or is it something that needs removing? Do the people building large language models pre-process their data to de-duplicate out these kinds of things from their current data sources?

bradleyjg 4y ago

I asked it to make a certain stand up bit in the style of various comedians. The Seinfeld one was reasonably good but the Chris Rock one was off because it wouldn’t curse.