I've been using this prompt on articles that generate debate. Like microservices, or jwt's. It brings up some interesting points for this article...
Look at this article and point out any wording that seems meant to push a certain viewpoint. Note anything important the author leaves out, downplays, or overstates, including numbers that seem cherry-picked or lack context. Clearly separate basic facts from opinions or emotional language. Explain how people with different viewpoints might read the article differently. Also call out any common persuasion tactics like loaded wording, selective quotes, or appeals to authority.
1. You assume that your LLM of choice is perfect and impartial on every given topic, ever.
2. You assume that your prompt doesn't interfere with said impartiality. What you have written may seem neutral at first glance, but from my perspective, a wording like yours would probably prime the model to try to pick apart absolutely anything, finding flaws that aren't really there (or make massive stretches) because you already presuppose that whatever you give it was written with intent to lie and misrepresent. The wording heavily implies that what you gave it already definitely uses "persuasion tactics", "emotional language" or that it downplays/overstates something - you just need it to find all that. So it will try to return anything that supports that implication.
It doesn't matter if you make assumptions or not - your prompt does. I think the point of failure isn't even necessarily the LLM, but your writing - because you leave the model no leeway or a way to report back on something truly neutral or impartial. Instead, you're asking it to dig up any proof of wrongdoing no matter what, basically saying that lies surely exist in whatever you post, and you just need help uncovering all the deception. When told to do this, it would read absolutely anything you give it in the most hostile way possible, stringing together any coherent-sounding arguments that would reinforce the viewpoint that your prompt implies.
I think this reads to me as a way for you to couch your ignorance as criticism while learning nothing from reading a study like this. Why not do this for your own biases?
What metrics do you focus on while reading an article that result in you confirming your own preconceived ideas?
If you have to come at an article like this in a hostile way, then you're not learning anythign about it, you're just confirming your own biases. I think I would recommend that you focus all of these criticisms inward at your own biases in terms of what you react to and need to explain and see if it's explained in the paper above. Then see if you find yourself convinced by the scientific method that they undertook?
Otherwise you're prepping yourself to continue living in an echo chamber.
Oh this is great news. After a $1000 bill running a model on vertex.ai continuously for a little test i forgot to shut down, this will be my go to now. I've been using Cloud Run for years running production microservices, and little hobby projects and i've found it simple and cost effective.
I'm in the same boat. I think i was geocities.com/Soho/???? right when it came out. I had Red Sox trivia questions, and it was multiple choice. The wrong answers linked to wrong.html, and the correct answer linked to 1.html, then 2.html etc. Fun times being a kid on the information super highway.
i remember you had a script that created animated images before that even was a thing. It exploited some kind of quirk in Netscape, must have been 1994-1996?
The first $5 i ever made online was on Compuserve. I was walking home from school (i think 1994) and i found a used Boston Bruins ticket stub on the ground. I put it on the classifieds section and sold it. The buyer sent me a $5 bill in the mail.
Just relying on blocking specific words isn't the best fix. You've gotta attack this problem from different angles. Big names like Doordash or Uber blast messages all the time, so you need a way to tell them apart from new senders who suddenly flood you. The bad guys will switch senders fast, so now you gotta find a way to fingerprint messages. They'll even tweak the text slightly, so you need fuzzy fingerprinting techniques. And then there's the headache of defining what's spam. Some folks see political donation requests or marketing pitches as spam, while others don't. Sorting these messages becomes a puzzle. Then you have to ask yourself if spam and scam are two distinct types of messages. Spotting scam messages means digging into their content, like checking URLs to sniff out if they're fishy. And phishing? That stuff looks real and can play out across a whole message chain.
You make some excellent points… but I wish I could block all text messages that have links in them. I know this wouldn’t work for everyone but it would be a brilliant option for me lol
The only text messages I get from humans are from old people and from coworkers. Humans don’t ever send me text messages with links.
The only legitimate links in text messages I get are from parcels tracking things and many scammers spoof those text messages.
Meanwhile I don’t think I’ve ever received a spam message that doesn’t contain a link, at least not in the last 5 years
The solution is extreme, and I’m sure some legit messages will get blocked but man I really wish on iOS there was a native way to block any messages containing a link.
I wonder if we'll ever get a San Andreas source code leak/release that would finally debunk or confirm the mystery of Bigfoot. After all these years, I still have hope that it's real...
Oh man I used to visit a subreddit every few months dedicated to this to make fun of people who were wasting tremendous amounts of time looking for something that clearly wasn’t there.
Look at this article and point out any wording that seems meant to push a certain viewpoint. Note anything important the author leaves out, downplays, or overstates, including numbers that seem cherry-picked or lack context. Clearly separate basic facts from opinions or emotional language. Explain how people with different viewpoints might read the article differently. Also call out any common persuasion tactics like loaded wording, selective quotes, or appeals to authority.