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Prompt Refusal

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Manage episode 372091188 series 49487
Content provided by Kyle Polich. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kyle Polich or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

The creators of large language models impose restrictions on some of the types of requests one might make of them. LLMs commonly refuse to give advice on committing crimes, producting adult content, or respond with any details about a variety of sensitive subjects. As with any content filtering system, you have false positives and false negatives.

Today's interview with Max Reuter and William Schulze discusses their paper "I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Models". In this work, they explore what types of prompts get refused and build a machine learning classifier adept at predicting if a particular prompt will be refused or not.

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599 episodes

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Prompt Refusal

Data Skeptic

3,171 subscribers

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Manage episode 372091188 series 49487
Content provided by Kyle Polich. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Kyle Polich or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

The creators of large language models impose restrictions on some of the types of requests one might make of them. LLMs commonly refuse to give advice on committing crimes, producting adult content, or respond with any details about a variety of sensitive subjects. As with any content filtering system, you have false positives and false negatives.

Today's interview with Max Reuter and William Schulze discusses their paper "I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Models". In this work, they explore what types of prompts get refused and build a machine learning classifier adept at predicting if a particular prompt will be refused or not.

  continue reading

599 episodes

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