Qualitatively, Figure 3 (left) shows 8 examples
generated by both preﬁx-tuning and ﬁne-tuning
models trained on different data levels. While both
methods tend to undergenerate (missing table con-
tents) in low data regimes, preﬁx-tuning tends to be
more faithful than ﬁne-tuning. For example, ﬁne-
tuning (100, 200)10 falsely claims a low customer
rating while the true rating is average, whereas
preﬁx-tuning (100, 200) generates a description
that is faithful to the table.
Prefix-Tuning