i − L+

Data Quality We qualitatively analyze some
API calls generated with our approach for different
APIs. Table 10 shows some examples of texts from
CCNet augmented with API calls, as well as the
corresponding score L−
that is used as a ﬁl-
tering criterion, and whether the API calls made by
the model are intuitively useful in the given context.
As can be seen, high values of L−
typically
correspond to useful API calls, whereas low values
correspond to API calls that do not provide any in-
formation that is useful for predicting future tokens.
There are some exceptions, e.g., an API call for

i − L+

i

i

T-REx

WebQS

k
0
1
3
10

–

All AC NC %
34.9
0.0
47.8 53.0 44.3 40.3
52.9 58.0 29.0 82.8
53.5 54.0 22.5 98.1

34.9

–

All AC NC %
0.0
18.9
18.9
8.5
19.3 17.1 19.9
26.3 26.5
6.6
99.3
26.3 26.4
100.0
–
Toolformer