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Show HN: Bullshit-detector – quickly detect amount of bullshit in any text
Hi, I have been reading marketing and business books recently and found plenty of them are filled with meaningless corporate jargon. These books could often be 1/3 of the original length and much more straightforward. I wrote a tiny library to calculate the amount of meaningless jargon in any text for myself, and open-sourced it later because someone may need this.
- pacifika 2y agoLove this
- r00tanon 2y agoBullshit.
- delichon 2y agoIt counts these phrases that the author doesn't like: https://github.com/pilotpirxie/bullshit-detector/blob/main/src/phrases.ts https://github.com/pilotpirxie/bullshit-detector/blob/main/s... By including this file this project should therefore correctly give itself a very high bullshit score. It's performance art really.
- dotancohen 2y agoThat is equivalent to noticing that antivirus applications would flag theme themselves as viruses if they would review their own virus samples. In applications I write, I store all example files separately for this reason and others. Even LLMs store the training data separately.
- spacebacon 2y agoMany duplicates
- bravetraveler 2y agoHaven't looked at the workings exactly, but this can sometimes be a deliberate choice to weight the options. A number property could work, but alas. Easier to yank and paste.
- spacebacon 2y agoI considered that. Ai also tends to list duplicates when generating exhaustive list.
- bravetraveler 2y agoTotally fair, just spittin' in the wind
- spacebacon 2y agoSame here lol
- deleted 2y ago[deleted]
- ilaksh 2y agoHm. I wonder how well this works versus a large LLM. Seems like something a very strong LLM should be able to handle well with the right prompting. If you can handle it with just phrases that would save a lot of time and money though.
- luke-stanley 2y agoYeah, presumably words with vague meanings, low semantic quality are easy to find. Still, a Bert model for it would be fun.
- luke-stanley 2y agoPresumably, the identified phrase list could be used to finetune a Bert model or similar that could catch more cases, as a binary classifier. But presumably some actual semantically meaningful words would be needed too. That would be straight forward to do too though. Someone has probably already done it. The advantage would be you could get probability metrics on a broader set of text. Good data is the key thing though.
- BrandoElFollito 2y agoThe fact that OP felt the need to add a disclaimer suggest that they expect people who write such abominations to search for detectors :) Who knows, though. Maybe there is a marketing dude who once thought "maybe that's too much?". Naah.