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A classifier supporting user-defined criteria. Functions well in content moderation use-cases as well as others such as brand voice checks and prompt injection detection. It combines classical ML w/ a consensus panel of small open LLMs, which allows for the accuracy associated w/ traditional classification methods, with the generality of LLMs. And because the LLMs have access to Wikipedia and Wolfram, they can navigate evaluations requiring up-to-date factual information as well as math.
I needed some fairly specific content moderation tooling for another project I'm working on, capable of capturing things like fallacies, cognitive distortions, persuasiveness, factual correctness, on-topic-ness, etc on top of more traditional moderation categories (i.e. harmful content). A lot of the existing services have fairly narrow categories for the content they can screen for, and tend to feel more enterprise / unapproachable (as a surface-level example, most want you to call them for a price). So I decided to build my own, starting w/ a pool of small, open LLMs as the basis for the consensus system, and threading them through more traditional ML approaches to draw conclusions from their individual votes. I was very happy with the results, and found that it worked w/ fairly high accuracy out-of-the-box for any category.
With the success of the system I decided to roll it out as an independent service. Many of the categories within its intended use-case are judgement calls where reasonable people could disagree, so I added a customization component, allowing the system to recognize the kinds of questions where its answers might disagree with the user's preferences, and adjust accordingly. Personalization is factored in at request time, so when a user submits feedback on a verdict or provides examples, the effect will be felt from the very next evaluation.
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About CriteriaBot on Product Hunt
“The Universal, Customizable Classifier”
CriteriaBot was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #115 on the daily leaderboard. A classifier supporting user-defined criteria. Functions well in content moderation use-cases as well as others such as brand voice checks and prompt injection detection. It combines classical ML w/ a consensus panel of small open LLMs, which allows for the accuracy associated w/ traditional classification methods, with the generality of LLMs. And because the LLMs have access to Wikipedia and Wolfram, they can navigate evaluations requiring up-to-date factual information as well as math.
CriteriaBot was featured in Developer Tools (515.5k followers), Artificial Intelligence (473.1k followers) and Tech (627.5k followers) on Product Hunt. Together, these topics include over 345.9k products, making this a competitive space to launch in.
Who hunted CriteriaBot?
CriteriaBot was hunted by Graham Paye. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Graham Paye