RNN-based text generation in NLP - Model Metrics & Evaluation

Metrics & Evaluation - RNN-based text generation

Which metric matters for RNN-based text generation and WHY

For RNN text generation, the main goal is to produce text that looks natural and meaningful. We often use perplexity to measure this. Perplexity tells us how well the model predicts the next word. A lower perplexity means the model is better at guessing the next word, so the generated text is more fluent.

Sometimes, we also check BLEU score if we have reference texts to compare. BLEU measures how similar the generated text is to real examples. But perplexity is the most common because it works even without exact references.

Confusion matrix or equivalent visualization

In text generation, we don't use a confusion matrix like in classification. Instead, we look at perplexity, which is calculated from the probabilities the model assigns to the correct next words.

Perplexity = exp(- (1/N) * sum(log P(w_i | context)))

Where:
- N is the number of words in the test set
- P(w_i | context) is the predicted probability of the actual next word

Lower perplexity means better prediction.
    

Precision vs Recall tradeoff with concrete examples

Precision and recall are not typical for text generation. Instead, we think about a tradeoff between creativity and coherence.

If the model is too safe (high coherence), it repeats common phrases and is boring. This is like high precision but low recall -- it only generates very safe words.

If the model is too creative (low coherence), it may produce strange or wrong words. This is like high recall but low precision -- it tries many words but many are bad.

Good text generation balances this tradeoff, producing text that is both interesting and makes sense.

What "good" vs "bad" metric values look like for RNN text generation

Good perplexity: Lower values, often between 20 and 50 for typical datasets, mean the model predicts next words well.

Bad perplexity: Very high values (100+) mean the model struggles to predict next words, so generated text is often nonsensical.

For BLEU (if used), scores closer to 1.0 mean generated text matches references well; scores near 0 mean poor match.

Common pitfalls in metrics for RNN text generation

• Overfitting: Very low perplexity on training data but high on test data means the model memorizes text and won't generalize.
• Ignoring diversity: Low perplexity alone doesn't guarantee interesting text; the model might repeat the same phrases.
• Using BLEU without references: BLEU needs reference texts; without them, it's not useful.
• Perplexity scale: Perplexity depends on vocabulary size and dataset; comparing across different setups can be misleading.

Self-check question

Your RNN text generation model has a perplexity of 25 on training data but 120 on test data. Is it good for generating natural text? Why or why not?

Answer: No, this is not good. The model performs well on training data but poorly on test data, showing it overfits. It memorizes training text but cannot generalize to new text, so generated text will likely be poor and unnatural.

Key Result

Perplexity is key: lower perplexity means the RNN predicts next words better, producing more natural text.