Know When To Stop: A Study of Semantic Drift in Text Generation
Ava Spataru Eric Hambro Elena Voita Nicola Cancedda
In this work, we explicitly show that modern LLMs tend to generate correct
facts first, then "drift away" and generate incorrect facts later: this was
occasionally observed but never properly measured. We develop a semantic drift
score that measures the degree of separation between correct and incorrect
facts in generated texts and confirm our hypothesis when generating
Wikipedia-style biographies. This correct-then-incorrect generation pattern
suggests that factual accuracy can be improved by knowing when to stop
generation. Therefore, we explore the trade-off between information quantity
and factual accuracy for several early stopping methods and manage to improve
factuality by a large margin. We further show that reranking with semantic
similarity can further improve these results, both compared to the baseline and
when combined with early stopping. Finally, we try calling external API to
bring the model back to the right generation path, but do not get positive
results. Overall, our methods generalize and can be applied to any long-form
text generation to produce more reliable information, by balancing trade-offs
between factual accuracy, information quantity and computational cost.