Humpback LLaMA
48.64
54.65
50.0
45.0
50.94
52.38
44.44
50.48
51.72
50.0

60.27
60.47
45.42
80.0
66.67
58.33
58.73
73.33
66.67
60.28

Table 10: Accuracy of detecting various types of biases in the CrowS-Pair benchmark.

4 Limitations

4.1 Bias

Since the augmented data is sourced from a web corpus, one potential consequence is that the finetuned
model could amplify biases from web data. We evaluate on the CrowS-Pairs dataset Nangia et al.
[2020] to measure the model’s performance in recognizing potential bias. Specifically, we evaluate
the accuracy in detecting biased statements in nine categories: gender, religion, race/color, sexual

11

orientation, age, nationality, disability, physical appearance and socioeconomic status. Compared to
the based model, our model has improved accuracy in detecting biases as is summarized in Table 10.
However, this does not mean our model is less likely to generate responses that contain biases.

4.2 Safety
Self-AlignmentwithInstructionBacktranslation