What are effective model-agnostic methods for detecting inappropriate outputs in text generation

Question

With the help of Python code snippets, can you name effective model-agnostic methods for detecting inappropriate outputs in text generation?

Answer 1

Effective methods for detecting inappropriate outputs in text generation are as follows:

• Rule-Based Filtering: It uses keyword matching or regex to flag offensive language.
• Toxicity Classifiers: It Utilizes pre-trained classifiers like Perspective API or Hugging Face toxicity models.
• Embedding-Based Similarity: It compares outputs against inappropriate content embeddings using cosine similarity.
• Human-in-the-Loop Review: It is used to Incorporate manual review for edge cases.

Here is an example of one of the methods: Toxicity Classifiers

This method is efficient for flagging inappropriate content in a model-agnostic way.

Hence, in this way, you can detect inappropriate outputs in text generation.