  What happens if we train a new Large Language Model (LLM) using data that are
at least partially generated by other LLMs? The explosive success of LLMs means
that a substantial amount of content online will be generated by LLMs rather
than humans, which will inevitably enter the training datasets of
next-generation LLMs. We evaluate the implications of such "regurgitative
training" on LLM performance. Through fine-tuning GPT-3.5 with data generated
either by itself or by other LLMs in a machine translation task, we find strong
evidence that regurgitative training clearly handicaps the performance of LLMs.
The same performance loss of regurgitative training is observed on transformer
models that we train from scratch. We find suggestive evidence that the
performance disadvantage of regurgitative training can be attributed to at
least two mechanisms: (1) higher error rates and (2) lower lexical diversity in
LLM-generated data as compared to real data. Based on these mechanisms, we
propose and evaluate three different strategies to mitigate the performance
loss of regurgitative training. First, we devise data-driven metrics to gauge
the quality of each LLM-generated data instance, and then carry out an ordered
training process where high-quality data are added before low-quality ones.
Second, we combine data generated by multiple different LLMs (as an attempt to
increase lexical diversity). Third, we train an AI detection classifier to
differentiate between LLM- and human-generated data, and include LLM-generated
data in the order of resemblance to human-generated data. All three strategies
can improve the performance of regurgitative training to some extent but are
not always able to fully close the gap from training with real data. Our
results highlight the value of real, human-generated data in training LLMs,
which cannot be easily substituted by synthetic, LLM-generated data.
