Automatic Detection of Generated Text isEasiest when Humans are Fooled
Table 2: Accuracy of BERT fine-tuned discriminator when trained on samples from one strategy (rows) and evaluated on another (columns). Trained on samples with 192 tokens. The ‘mixed’ dataset is one containing an equal portion of samples from each strategy.
['[EMPTY]', '[EMPTY]', 'Eval top- [ITALIC] k', 'Eval nucleus', 'Eval random']
Unsurprisingly a discriminator trained on top-k generalizes poorly to other sampling methods : accuracy drops to as low as 42.5%, worse than chance. Conversely, training the discriminator with sequences sampled from the untruncated distribution leads to little transferability to detecting top-k samples. Only the discriminator trained with nucleus sampling (a compromise between unmodified sampling and top-k) was able to detect sequences from the other sampling strategies without too much of a hit to accuracy. As expected, a discriminator trained on an equal portion of data from each decoding method does reasonably at detecting all three.