Evaluating $n$-Gram Novelty of Language Models Using Rusty-DAWG
<|reference_start|>Evaluating $n$-Gram Novelty of Language Models Using Rusty-DAWG: How novel are texts generated by language models (LMs) relative to their training corpora? In this work, we investigate the extent to which modern LMs generate $n$-grams from their training data, evaluating both (i) the probability LMs assign to complete training $n$-grams and (ii) $n$-novelty, the proportion of $n$-grams generated by an LM that did not appear in the training data (for arbitrarily large $n$). To enable arbitrary-length $n$-gram search over a corpus in constant time w.r.t. corpus size, we develop Rusty-DAWG, a novel search tool inspired by indexing of genomic data. We compare the novelty of LM-generated text to human-written text and explore factors that affect generation novelty, focusing on the Pythia models. We find that, for $n > 4$, LM-generated text is less novel than human-written text, though it is more novel for smaller $n$. Larger LMs and more constrained decoding strategies both decrease novelty. Finally, we show that LMs complete $n$-grams with lower loss if they are more frequent in the training data. Overall, our results reveal factors influencing the novelty of LM-generated text, and we release Rusty-DAWG to facilitate further pretraining data research.<|reference_end|>
@article{merrill2024evaluating,
    title={Evaluating $n$-Gram Novelty of Language Models Using Rusty-DAWG},
    author={William Merrill and Noah A. Smith and Yanai Elazar},
    journal={arXiv preprint arXiv:2406.13069},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13069},
    primaryClass={cs.CL cs.AI}
}