1. The abstract should be expanded to encompass key concepts that effectively summarize the paper's contributions. In the introduction, the authors emphasize the significance of interpretability and the challenges it poses in achieving high accuracy. By including these vital points in the abstract, the paper can provide a more comprehensive overview of its content and contributions.
2. Regarding the abstention process, it appears to be based on a prediction probability threshold, where if the probability is lower than the threshold, the prediction is abstained? How does it different from a decision threshold used by the models? Can authors clarify that?
3. In the results and discussion section, there's limited exploration and commentary on the impact of the solution on system accuracy, as seen in Table 2. Notably, the confirmation budget appears to have a limited effect on datasets like "noisyconcepts25" and "warbler" compared to others. The paper can delve into the reasons behind this discrepancy.
4. In real-world applications of this solution, questions about the ease of concept approval and handling conflicting user feedback arise. While these aspects may be considered out of scope, addressing them would be beneficial for evaluating the practicality of implementing this approach in real-world scenarios. This is particularly important when considering the potential challenges of user feedback and conflicting inputs in such applications.
Minor things:
Page 4, confirm. we —> replace . with comma
Section 4.2, Table Table 2 —> Table 2
Shouldn’t Table 2 rather be labelled as Figure 2?
2. Regarding the abstention process, it appears to be based on a prediction probability threshold, where if the probability is lower than the threshold, the prediction is abstained? How does it different from a decision threshold used by the models? Can authors clarify that?