TokenDisparity

Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and bar charts, alongside compiling a comprehensive table of descriptive statistics for token counts.

Purpose: This function is designed to assess the token disparity between reference and generated texts. Token disparity is important for understanding how closely the length and token usage of generated texts match the reference texts.

Test Mechanism: The function starts by extracting the true and predicted values from the provided dataset and model. It then calculates the number of tokens in each reference and generated text. Histograms and bar charts are generated for the token counts of both reference and generated texts to visualize their distribution. Additionally, a table of descriptive statistics (mean, median, standard deviation, minimum, and maximum) is compiled for the token counts, providing a comprehensive summary of the model’s performance.

Signs of High Risk: - Significant disparity in token counts between reference and generated texts could indicate issues with text generation quality, such as verbosity or lack of detail. - Consistently low token counts in generated texts compared to references might suggest that the model is producing incomplete or overly concise outputs.

Strengths: - Provides a simple yet effective evaluation of text length and token usage. - Visual representations (histograms and bar charts) make it easier to interpret the distribution and trends of token counts. - Descriptive statistics offer a concise summary of the model’s performance in generating texts of appropriate length.

Limitations: - Token counts alone do not provide a complete assessment of text quality and should be supplemented with other metrics and qualitative analysis.