The evaluation metric for text generation

ChatGPT, OpenAI

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For text generation, the evaluation metric often depends on the specific task and desired outcomes. However, some common evaluation metrics used in NLP for text generation tasks include:

1. Perplexity:
   • Definition: Perplexity measures how well a probability model predicts a sample. In the context of language models, lower perplexity indicates a better predictive model.
   • Usage: It is widely used to evaluate language models (e.g., GPT-2) by measuring the model's ability to predict a sequence of words.
   • Limitation: It does not directly measure the quality or relevance of generated text; it only measures the fluency of the text.
2. BLEU Score (Bilingual Evaluation Understudy):
   • Definition: BLEU is a precision-based metric that compares the generated text to reference texts (often human-written) by measuring n-gram overlap.
   • Usage: It is commonly used for evaluating machine translation but can also be applied to text generation tasks where there are reference texts for comparison.
   • Limitation: BLEU has limitations when used for creative or open-ended text generation, as it penalizes diverse yet valid outputs that differ from the reference.
3. ROUGE Score (Recall-Oriented Understudy for Gisting Evaluation):
   • Definition: ROUGE measures the overlap of n-grams, word sequences, and word pairs between the generated text and reference text. ROUGE-N (e.g., ROUGE-1, ROUGE-2) focuses on recall, while ROUGE-L considers the longest common subsequence.
   • Usage: It is commonly used to evaluate summarization but can be applied to text generation tasks.
   • Limitation: Like BLEU, ROUGE may not always capture the quality of diverse and creative generated text.
4. METEOR (Metric for Evaluation of Translation with Explicit ORdering):
   • Definition: METEOR is based on precision and recall with more relaxed matching, considering synonyms and stemming. It aims to improve upon BLEU's shortcomings.
   • Usage: Primarily used for machine translation, but it can also be relevant for other text generation tasks where reference texts are available.
   • Limitation: Requires reference texts for comparison.
5. CHR-F (Character n-gram F-score):
   • Definition: A character-level metric that calculates the F-score based on character n-gram overlap.
   • Usage: Useful for evaluating text generation in tasks where character-level accuracy is important.
   • Limitation: May not fully capture the semantic quality of generated text.
6. Human Evaluation:
   • Definition: Human raters evaluate the generated text based on fluency, coherence, relevance, informativeness, and creativity.
   • Usage: Often considered the gold standard for evaluating text generation tasks, especially for open-ended tasks like story generation, dialogue generation, or creative writing.
   • Limitation: Expensive and time-consuming compared to automatic metrics.

Conclusion:

For your project, if you are focusing on text generation, consider using Perplexity for evaluating language fluency, or BLEU, ROUGE, or METEOR if you have reference texts. For a more comprehensive evaluation, combining automatic metrics with human evaluation would provide a more holistic understanding of the generated text's quality.