seq2seq与Transformer模型中Testing、Evaluation、Inference的区别解析

Hey there! It makes total sense to get confused with these terms—they’re often used interchangeably in tutorials, but they refer to distinct workflows, especially with autoregressive models like Transformers. Let’s unpack each one clearly, then tackle why your model is performing well in evaluation/testing but failing at inference.

First: Core Definitions #

Evaluation #

Your initial understanding wasn’t wrong—it’s just one type of evaluation. Here’s the full picture:

• Offline/Teacher-Forced Evaluation: This is what your tutorial showed. You feed both source data and the full target sequence to the model, then compute loss (like cross-entropy) via forward propagation. This is fast because it uses "teacher forcing" (the model always sees the real next token instead of its own predictions), so it’s great for quickly checking if the model learned the general pattern of the data. It gives you metrics like perplexity that measure how well the model predicts known targets.
• Online/Generative Evaluation: This is what you originally thought evaluation was. Here, you only feed the source data, let the model autoregressively generate the target sequence (just like inference), then compare the generated output to the real target using metrics like BLEU, ROUGE, or human evaluation. This is slower but more reflective of real-world performance.

Inference #

This is the "real-world" mode of your model: you only provide the source input, and the model generates the target sequence token-by-token, with each next prediction based on the previous ones (no real target data to guide it). This is what happens when you deploy the model for actual use (e.g., translation, summarization). The key difference from teacher-forced evaluation is that inference relies on the model’s own predictions to continue generating, which can amplify small errors over time.

Testing #

This is more about data partitioning than a specific operation. The "test set" is a portion of your data that the model never saw during training or validation. Testing just means running either type of evaluation (teacher-forced or generative) on this held-out set to get an unbiased measure of the model’s generalization ability. So when people say "testing the model," they might be referring to either computing loss with the full target sequence or running inference and checking generated outputs.

Why Your Model Performs Well in Evaluation/Testing but Fails at Inference #

If your model does great when you feed it target data for evaluation but can’t generate meaningful outputs during inference, here are the most common culprits:

• Over-reliance on Teacher Forcing: During training, if you only used teacher forcing, the model never learned to recover from its own mistakes. When it generates a wrong token during inference, all subsequent predictions are based on that error, leading to gibberish. Try adding scheduled sampling to training—occasionally replace the real target token with the model’s prediction to help it adapt to autoregressive generation.
• Suboptimal Decoding Strategy: Greedy search (picking the highest-probability token every time) often leads to repetitive or nonsensical outputs. Experiment with beam search (keeping track of top-N most likely sequences) or top-k/top-p sampling for more natural generation. Adjust parameters like beam size or temperature to fine-tune results.
• Tokenization/Preprocessing Mismatch: Double-check that your inference pipeline uses the exact same tokenizer settings (vocabulary, padding, truncation, special tokens) as your training pipeline. A small mismatch (e.g., forgetting to add a <EOS> token at inference time) can break generation.
• Overfitting to the Training Data: Even if the model has low loss on the test set, it might have memorized patterns instead of learning generalizable ones. Generative inference exposes this because it requires the model to create new sequences, not just predict known tokens. Try adding regularization (dropout, weight decay) or using a larger, more diverse dataset.

Quick Fixes to Try #

1. Swap from greedy search to beam search in your inference code (start with a beam size of 5-10).
2. Add scheduled sampling to your training loop—start with a high teacher forcing ratio and gradually reduce it over epochs.
3. Evaluate using generative metrics (like BLEU) instead of just loss—this will give you a better sense of how the model performs in inference mode during training.
4. Debug the inference pipeline step-by-step: print the input tokens, the model’s intermediate predictions, and the final output to spot where things go wrong.

内容的提问来源于stack exchange，提问作者Natalia