ToxicityScore

Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content.

Purpose

The ToxicityScore metric is designed to evaluate the toxicity levels of texts generated by models. This is crucial for identifying and mitigating harmful or offensive content in machine-generated texts.

Test Mechanism

The function starts by extracting the input, true, and predicted values from the provided dataset and model. The toxicity score is computed for each text using a preloaded toxicity evaluation tool. The scores are compiled into dataframes, and histograms and bar charts are generated to visualize the distribution of toxicity scores. Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and maximum) is compiled for the toxicity scores, providing a comprehensive summary of the model’s performance.

Signs of High Risk

• Drastic spikes in toxicity scores indicate potentially toxic content within the associated text segment.
• Persistent high toxicity scores across multiple texts may suggest systemic issues in the model’s text generation process.

Strengths

• Provides a clear evaluation of toxicity levels in generated texts, helping to ensure content safety and appropriateness.
• Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of toxicity scores.
• Descriptive statistics offer a concise summary of the model’s performance in generating non-toxic texts.

Limitations

• The accuracy of the toxicity scores is contingent upon the underlying toxicity tool.
• The scores provide a broad overview but do not specify which portions or tokens of the text are responsible for high toxicity.
• Supplementary, in-depth analysis might be needed for granular insights.