How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN

Understand

Current language models can generate high-quality text.

• Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce RAVEN, a suite of analyses for assessing the novelty of generated text, focusing on sequential structure (n-grams) and syntactic structure.
• We apply these analyses to four neural language models (an LSTM, a Transformer, Transformer-XL, and GPT-2).
• For local structure - e.g., individual dependencies - model-generated text is substantially less novel than our baseline of human-generated text from each model's test set.