There's some interesting technical details in this release:
> Privacy Filter is a bidirectional token-classification model with span decoding. It begins from an autoregressive pretrained checkpoint and is then adapted into a token classifier over a fixed taxonomy of privacy labels. Instead of generating text token by token, it labels an input sequence in one pass and then decodes coherent spans with a constrained Viterbi procedure.
> The released model has 1.5B total parameters with 50M active parameters.
> [To build it] we converted a pretrained language model into a bidirectional token classifier by replacing the language modeling head with a token-classification head and post-training it with a supervised classification objective.
For the confused: this link must have gotten revived or something, I posted this comment a few days ago. Looks like it's getting the accolades I claim it deserves now.
It's going to be stochastic in some sense whether you want it to be or not, human error never reaches zero percent. I would bet you a penny you'd get better results doing one two-second automated pass + your usual PII redaction than your PII redaction alone.
The advantage of computers was that they didn't make human errors; they did things repeatedly, quickly, and predictably. If I'm going to accept human error, I'd like it to come from a human.
I think the problem is most secrets arn't stochastic; they're determinant. When the user types in the wrong password, it should be blocked. Using a probabilistic model suggests an attacker only now needs to be really close, but not correct.
Sure, there's some math that says being really close and exact arn't a big deal; but then you're also saying your secrets don't need to be exact when decoding them and they absolutely do atm.
Sure looks like a weird privacy veil that sorta might work for some things, like frosted glass, but think of a toilet stall with all frosted glass, are you still comfortable going to the bathroom in there?
I dunno what use case you're thinking this is for.
The use case for this is that many enterprise customers want SaaS products to strip PII from ingested content, and there's no non-model way to do it.
Think, ingesting call transcripts where those calls may include credit card numbers or private data. The call transcripts are very useful for various things, but for obvious reasons we don't want to ingest the PII.
Exciting! I took a look through the code and found what appear to be the entity types for future releases - this release (V2 config) supports 8 entity types, but the V4 and V7 taxonomies have >20, mostly more personal ID types. Given this is a preview release, I imagine they'll release these.
For my customers I use regexes to block them from potentially publishing personal emails/phone numbers to their websites but I really wouldn't mind running this in addition just for the extra peace of mind. I don't have a GPU on our server, but I hope this is light enough of a model to handle CPU only inference on less than 2k tokens at a time.
50M effective parameters is impressively light. Is there a similarly light model on the prompt injection side? Most of the mainstream ones seem heavier
SuperagentLM made available on-edge PPI redaction models already a few years ago in sizes 20B, 3B, 200M. They still seem to be available via their legacy API - well worth checking out to compare against this one.
https://docs.superagent.sh/legacy/llms/superagent-lm-redact-...
> Privacy Filter is a bidirectional token-classification model with span decoding. It begins from an autoregressive pretrained checkpoint and is then adapted into a token classifier over a fixed taxonomy of privacy labels. Instead of generating text token by token, it labels an input sequence in one pass and then decodes coherent spans with a constrained Viterbi procedure.
> The released model has 1.5B total parameters with 50M active parameters.
> [To build it] we converted a pretrained language model into a bidirectional token classifier by replacing the language modeling head with a token-classification head and post-training it with a supervised classification objective.
1. Pass the raw text through the filter to obtain the spans.
2. Map all the spans back to the original text.
Now you have all the PII information.
https://github.com/chiefautism/privacy-parser
Sure, there's some math that says being really close and exact arn't a big deal; but then you're also saying your secrets don't need to be exact when decoding them and they absolutely do atm.
Sure looks like a weird privacy veil that sorta might work for some things, like frosted glass, but think of a toilet stall with all frosted glass, are you still comfortable going to the bathroom in there?
The use case for this is that many enterprise customers want SaaS products to strip PII from ingested content, and there's no non-model way to do it.
Think, ingesting call transcripts where those calls may include credit card numbers or private data. The call transcripts are very useful for various things, but for obvious reasons we don't want to ingest the PII.
Details in my review article here: https://piieraser.ai/blog/openai-privacy-filter. Disclaimer: I also build PII detection systems.
Bringing back the Open to OpenAI..