General Response (2/2)
It can be seen from the results that our conclusions are retained when BLOOD is applied to Vision Transformer (ViT), BLOOD_{L-1} works better than BLOOD_mean and BLOOD performs better on more complex cifar100 than on simpler dataset cifar10. However, it seems that BLOOD does not retain its effectiveness when paired with CNNs, i.e., ResNet34 and ResNet50. Also, for CNNs, BLOOD_mean works fine for detecting OOD data, while BLOOD_{L-1} is not discriminative between OOD and ID data. For more details, please see Appendix D of our updated paper.
There are inherent differences in how different architectures process data, which impact the availability of different OOD detection methods. In our experiments and the experiments in [1], the best performing OOD methods for Transformer-based language models are distance-based methods (e.g., KNN and Mahalanobis distance), while methods that are SOTA on CNNs (ReAct, ASH, DICE) often underperform even against the simple baselines. In [1], DICE is by far the worst-performing OOD detection method for Transformer-based LLMs. Conversely, in the original paper of ASH [2], the authors did a comprehensive OOD detection experiment on CNNs, which showed Mahalanobis distance by far underperforming compared to other methods. In light of the finding that the choice of architecture plays a crucial role in the success of the OOD detection method, we updated our paper to reflect that Transformers are the scope of our work.
**3) Relationship between BLOOD_mean and BLOOD_{L-1}** - Reviewers R2 and R3 were interested in the reasoning behind BLOOD-mean being the worst choice most of the time, except in a few instances. Firstly, let us explain why BLOOD_mean underperforms (especially for RoBERTa). Figure 1 and similar figures in the Appendix for other datasets (Figures 4-10) show how BLOOD changes across layers. Comparing the subfigures it can be seen that since there is almost no change after training in the lower layers, the BLOOD in those layers stays the same as before training, where there was no difference between the ID and OOD data (because the model has not yet seen the ID data). However, in those lower layers for RoBERTa the BLOOD scores have larger magnitudes than in higher layers where the difference in BLOOD of ID and OOD data is prominent. Thus, by averaging the BLOOD scores of different layers the noisy lower layers dominate the informative higher layer and make BLOOD less discriminative between ID and OOD data. Since for ELECTRA the higher and more informative layers have higher magnitudes of BLOOD score, this effect is less noticeable.
However, the reviewers have noticed that in some cases, e.g., on MG and NG datasets with RoBERTa, BLOOD_mean archives better results. We can look at Figures 9 and 10 to explain this effect. Looking at subfigures (c) it can be seen that there is a difference in BLOOD scores between ID and OOD data across layers even before fine-tuning on the ID data, likely due to inherent differences in ID and OOD data (we speculate it could be due to the length of documents, as the longest documents from our experiments come from MG and NG datasets). It can be seen that in low layers OOD data has higher BLOOD scores than ID data, and that difference is retained even after fine-tuning, as those layers are not changed much. Thus, including the lower layers helps discriminate between ID and OOD data.
However, we would like to note that it is still better and more consistent to use BLOOD_{L-1} than BLOOD_mean, since this difference in the earlier layers could have gone the other way around and impact the performance of BLOOD_mean in a negative way.
We have addressed all other comments in individual responses to each reviewer. The reviews have significantly improved our paper, for which we are thankful.
*As abbreviations, we refer to reviewers **Txyj** as R1, **Bnog** as R2, **woCv** as R3, and **ELpL** as R4 respectively.
[1] Mateusz Baran, Joanna Baran, Mateusz Wójcik, Maciej Zięba, and Adam Gonczarek. 2023. Classical Out-of-Distribution Detection Methods Benchmark in Text Classification Tasks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
[2] Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, and Rosanne Liu. Extremely simple activation shaping for out-of-distribution detection. In The Eleventh International Conference on Learning Representations, ICLR 2023