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ZipPy: Fast method to classify text as AI or human-generated

This is a research repo for fast AI detection using compression. While there are a number of existing LLM detection systems, they all use a large model trained on either an LLM or its training data to calculate the probability of each word given the preceding, then calculate a score where the more high-probability tokens are more likely to be AI-originated. Techniques and tools in this repo are looking for faster approximation to be embeddable and more scalable.

Compression-based detector (`zippy.py` and `nlzmadetect`)

ZipPy uses either the LZMA or zlib compression ratios as a way to indirectly measure the perplexity of a text. Compression ratios have been used in the past to detect anomalies in network data for intrusion detection, so if perplexity is roughly a measure of anomalous tokens, it may be possible to use compression to detect low-perplexity text. LZMA and zlib create a dictionary of seen tokens and then use though in place of future tokens. The dictionary size, token length, etc. are all dynamic (though influenced by the 'preset' of 0-9--with 0 being the fastest but worse compression than 9). The basic idea is to 'seed' a compression stream with a corpus of AI-generated text (ai-generated.txt) and then measure the compression ratio of just the seed data with that of the sample appended. Samples that follow more closely in word choice, structure, etc. will achieve a higher compression ratio due to the prevalence of similar tokens in the dictionary, novel words, structures, etc. will appear anomalous to the seeded dictionary, resulting in a worse compression ratio.

Current evaluation

Some of the leading LLM detection tools are: ~~OpenAI's model detector (v2)~~, Content at Scale, GPTZero, CrossPlag's AI detector, and Roberta. Here are each of them compared with both the LZMA and zlib detector across the test datasets:

Installation

You can install zippy one of two ways:

Using python/pip

Via pip:

pip3 install thinkst-zippy

Or from source:

python3 setup.py build && python3 setup.py sdist && pip3 install dist/*.tar.gz

Now you can import zippy in other scripts.

Using pkgx

pkgx install zippy # or run it directly `pkgx zippy -h`

Usage

ZipPy will read files passed as command-line arguments or will read from stdin to allow for piping of text to it.

Once you've installed zippy it will add a new script (zippy) that you can use directly:

$ zippy -h
usage: zippy [-h] [-p P] [-e {zlib,lzma,brotli,ensemble}] [-s | sample_files ...]

positional arguments:
  sample_files          Text file(s) containing the sample to classify

options:
  -h, --help            show this help message and exit
  -p P                  Preset to use with compressor, higher values are slower but provide better compression
  -e {zlib,lzma,brotli,ensemble}
                        Which compression engine to use: lzma, zlib, brotli, or an ensemble of all engines
  -s                    Read from stdin until EOF is reached instead of from a file
$ zippy samples/human-generated/about_me.txt 
samples/human-generated/about_me.txt
('Human', 0.06013429262166636)

If you want to use the ZipPy technology in your browser, check out the Chrome extension or the Firefox extension that runs ZipPy in-browser to flag potentially AI-generated content.

3.9 KiB Czysty Bezpośredni odnośnik Wina Historia