Devise a method to losslessly compress the following text. Then apply that method to this text. Please output detailed instructions for compression, detailed instructions for decompression, and the compressed text. Follow all this by a summary of the compression ratio achieved.
If this is the case and we assume there is no state shared between the summarization phase and the expansion phase, doesn't this suggest that this is just a giant "over-fit" ml model, more like a compression algorithm (a very inefficient one), and is incapable of any original thought etc? This is my hypothesis. I mean another giant downside of the LLMs today is the fact that they are incapable of learning without throwing massive amounts of money and compute at the problem which I assume would be retraining ALL the weights from scratch. You could retrain part of the weights but that would no doubt negatively affect "unconnected" (in a human sense) parts of the model. Is this even sustainable at all and will it ever be?
Response:
To compress the given text, we will use the following method:
1. Identify and replace repeating phrases with shorter tokens.
2. Remove unnecessary spaces and punctuation marks.
3. Replace frequently occurring words with shorter tokens.
Compression Instructions:
1. Replace "this is" with "A", "the fact that" with "B", "in a human sense" with "C", "at all" with "D", "of the" with "E", and "and the" with "F".
2. Remove spaces before and after punctuation marks.
3. Replace "the" with "1", "and" with "2", "is" with "3", "of" with "4", "that" with "5", "a" with "6", "to" with "7", "which" with "8", "this" with "9", "assume" with "0".
Compressed Text:
If A case 2 we 0 there 3 no state shared between E summarization phase F expansion phase,doesn't 9 suggest 5 9 3 just 6 giant "over-fit" ml model, more like 6 compression algorithm(a very inefficient one),2 3 incapable 4 any original thought etc?9 3 my hypothesis. I mean another giant downside 4 E LLMs today 3 B 5 1y are incapable 4 learning without throwing massive amounts 4 money 2 compute 7 E problem 8 I 0 would be retraining ALL E weights from scratch. You could retrain part 4 E weights but 5 would no doubt negatively affect "unconnected" (C) parts 4 E model. 3 9 even sustainable D 2 will it ever be?
Decompression Instructions:
1. Replace "A" with "this is", "B" with "the fact that", "C" with "in a human sense", "D" with "at all", "E" with "of the", and "F" with "and the". 2. Add spaces before and after punctuation marks. 3. Replace "1" with "the", "2" with "and", "3" with "is", "4" with "of", "5" with "that", "6" with "a", "7" with "to", "8" with "which", "9" with "this", "0" with "assume".
Summary:
The original text has 692 characters, and the compressed text has 439 characters. The compression ratio achieved is approximately 1.58 (692/439).
...and then when I took only the decompression instructions and text to GPT 3.5:
Decompress the following text exactly according to the given instructions:
Compressed Text:
If A case 2 we 0 there 3 no state shared between E summarization phase F expansion phase,doesn't 9 suggest 5 9 3 just 6 giant "over-fit" ml model, more like 6 compression algorithm(a very inefficient one),2 3 incapable 4 any original thought etc?9 3 my hypothesis. I mean another giant downside 4 E LLMs today 3 B 5 1y are incapable 4 learning without throwing massive amounts 4 money 2 compute 7 E problem 8 I 0 would be retraining ALL E weights from scratch. You could retrain part 4 E weights but 5 would no doubt negatively affect "unconnected" (C) parts 4 E model. 3 9 even sustainable D 2 will it ever be?
Decompression Instructions:
1. Replace "A" with "this is", "B" with "the fact that", "C" with "in a human sense", "D" with "at all", "E" with "of the", and "F" with "and the".
2. Add spaces before and after punctuation marks.
3. Replace "1" with "the", "2" with "and", "3" with "is", "4" with "of", "5" with "that", "6" with "a", "7" with "to", "8" with "which", "9" with "this", "0" with "assume".
I got...
This is the text after decompression:
If this is a case and we assume there is no state shared between of the summarization phase and the expansion phase, doesn't this suggest that this is just a giant "over-fit" ml model, more like a compression algorithm (a very inefficient one), and is incapable of any original thought etc? This is my hypothesis. I mean another giant downside of the LLMs today is the fact that they are incapable of learning without throwing massive amounts of money to compute and the problem which I assume would be retraining ALL of the weights from scratch. You could retrain part of the weights but that would no doubt negatively affect "unconnected" (in a human sense) parts of the model. Is this even sustainable at all and will it ever be?
Still two mistakes: it dropped the fourth word, "the" in the initial compression. Then the decompressor ignored my "exactly" and filled it in as "a" rather than just leaving the sentence broken.
I still wouldn't use GPT for anything where you're looking for an exact result, I guess. But it's not quite the trainwreck you saw (or which I also saw, with your original prompt, where we made it invent a scheme without looking at any text first).
Probably better to get it to help you write the code for the compression scheme, then run that, if you're going to use it for such a thing at all.