Comment by usgroup | Hacker News Reader

Comment by usgroup | Hacker News Reader

Just reading the wikipedia page on Base64 (https://en.wikipedia.org/wiki/Base64). Algorithmically it seems to be a 8-bit byte to character mapping. So, if you were to base encode a character string it'd be {characters} -> {bytes} ->[base64] -> {characters}.

I'd presume this prompt would work fine:

Here is a mapping of numbers to letter given as a pairs: (A,1) (B,7) (C,23) (D,14). Use the mapping to convert the following string to numeric representation: ACCDAB.

I'd also presume that the same template could be used to instruct ChatGPT to do Base64 (with some additions for padding).

Yet, I suspect the capability given in the above prompt is not surprising to anyone. So, why is Base64 surprising? What am I missing?

>Algorithmically it seems to be a 8-bit byte to character mapping

Base64 is 6-bits, so there is no mapping of a single character to another single character. Changing 1 character in the input can change 1 or 2 characters in the output because each of the 6-bit "characters" are up against each other. So there is no mapping of input character to output character(s), it depends on context.

I'm aware that regular NL translations are a similar problem, but with base64, it's a much more exact and unforgiving problem, and GPT-4 seems to perform at a very high level.

Ok I see, but that doesn't strike me as different in kind, is it? I.e. bytes to 6-bit sequences for which there is a mapping table. Now you have a string of 6 symbol sequences. Then divide the string into 8 symbol sequences and map each to a character (again via mapping table). Ignoring padding rules, its two mappings, one followed by the other?

One can indeed argue that it’s not surprising – depending on your prior understanding of how Base64 works.

But for a person where both Base64 and LLMs are black boxes, then it can indeed be surprising.

To me, the most surprising thing about ChatGPT and other LLMs are the quality (despite all their flaws) and ther general applicability to a broad range of diverse tasks.

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