Principle:Gretelai Gretel synthetics Synthetic Text Generation
| Knowledge Sources | |
|---|---|
| Domains | Synthetic_Data, Deep_Learning, Text_Generation |
| Last Updated | 2026-02-14 19:00 GMT |
Overview
Synthetic text generation is the process of using a trained language model to autoregressively produce new text sequences that are statistically similar to the training data but are not copies of any training record.
Description
Once an LSTM model has been trained on tokenized text, synthetic text generation uses the learned probability distributions to produce new text one token at a time. The process involves:
- Seed initialization: Generation begins with a seed string (start string). This can be a simple newline token (generating from scratch) or a user-provided prefix that constrains the initial context. For structured data with field delimiters, the seed must end with the delimiter so the model knows which column to predict next.
- Autoregressive sampling: At each step, the model takes the sequence generated so far, predicts a probability distribution over the vocabulary for the next token, and samples from that distribution. The sampled token is appended to the sequence, and the process repeats.
- Temperature-controlled randomness: A temperature parameter scales the logits before softmax, controlling the entropy of the output distribution. Low temperatures produce more deterministic (repetitive) text; high temperatures produce more diverse (but potentially less coherent) text.
- Termination conditions: Generation of a single line stops when a newline token is predicted or when a maximum character limit is reached.
- Validation and filtering: An optional line validator function inspects each generated record. Valid records count toward the requested line count; invalid records are tracked and generation continues until enough valid records are produced or a maximum invalid threshold is exceeded.
- Batch and parallel generation: For throughput, the model generates multiple sequences in parallel within a single batch (predict_batch_size). An additional parallelism parameter can spawn multiple workers, each generating a chunk of the total records.
Usage
Use synthetic text generation when:
- You need to produce synthetic datasets that preserve the statistical properties of the training data without reproducing actual records.
- You want to augment limited training data for downstream tasks.
- You need to generate structured/delimited synthetic records with field-level validation.
- You want to seed generation with specific prefixes to control the output.
Theoretical Basis
Autoregressive generation decomposes the joint probability of a sequence into a product of conditional probabilities:
P(t_1, t_2, ..., t_n) = product_{i=1}^{n} P(t_i | t_1, ..., t_{i-1})
At each step, the model produces logits z over the vocabulary. Temperature scaling modifies these before sampling:
P(t_i = k) = exp(z_k / tau) / sum_j exp(z_j / tau)
where tau is the temperature. As tau -> 0, the distribution becomes a point mass on the highest-probability token (greedy decoding). As tau -> infinity, the distribution approaches uniform.
Categorical sampling draws the next token from the adjusted distribution:
t_i ~ Categorical(softmax(z / tau))
Stateful batch generation maintains LSTM hidden states across prediction steps. For a batch of B sequences, the model predicts the next token for all B sequences simultaneously. States can optionally be reset between records (reset_states=True) to ensure independence between generated records, at some cost to coherence.
Validation loop enforces quality by rejecting records that fail user-defined criteria:
valid_count = 0
invalid_count = 0
while valid_count < num_lines:
record = generate_one_line()
if validator(record):
valid_count += 1
yield record
else:
invalid_count += 1
if invalid_count > max_invalid:
raise TooManyInvalidError