Supervised
XS
0.0
+0.1
+1.1
+3.4
+3.0
+2.6

SG
0.0
+0.3
+4.0
+8.7
+1.8
+5.2
+408 +1850
-2.6
+11.3 +13.4 +0.5 +2.5
+2.3
+950
-1.8
+16.5 +10.2 +0.6 +3.1
+15.7 +12.5 +0.5 +2.6 +1.3
+522 +3550
+365 +1950
+14.2 +10.0 +0.7 +2.7 +1.6
+17.4 +13.1 +1.2 +3.5 +5.3 +1754 +373 +3150

One-shot
All Win
LM
SG
XS
SGD TOT
TOT
SGD
-
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8/9
-8.0
+21.4
+164 +3100
+0.2 +0.2 +8.5 +74.3
7/9
-2.5
+393 +2100
+274
+21.0
-7.0
+0.5 +0.7
-54.2 +13.9
7/9
-1.8
+0.1 +1.8
+87.0 +57.1
+700
-1.3
+15.8
6/9
+0.5 +1.0 +4.0
+387
-9.3
-50
+0.6 +1.7 +7.9 +1190 +350 +2800 +0.3 +45.7
9/9
7/9
-2.9
+48.6
-208 +31.7
7/9
8/9
+60.3
-2.2
-12.9 +52.6
8/9
-8.3
+76.1
8/9

+946
+1107
+726
+974

4.3 Overview of Ablative Experimental Results
Table 2 reports the raw results on all the benchmark tasks and datasets. To facilitate easier comparison across
setups, we also report relative comparisons against well-established baselines such as T5 and GPT models.
This is reported in Tables 3 and 4 respectively.
UL2- Unifying Language Learning Paradigms