Paper: Arithmetic with Language Models: from Memorization to Computation (ICLR-2024)

Reviewer comment (Novelty):
The main weakness of this paper, in my opinion, is that it does not engage with the model interpretability literature (neither in “mechanistic interpretability” or “probing”).

Author response:
Hereafter we reply to your specific Questions. Please also have a look at the global responses where we summarize our general comments. 

Q1: Thank you very much for the detailed review and the relevant references provided. As explained in the general comments a new version has been uploaded where the work is better framed into the interpretability literature.

Moreover, we fixed the minor presentation issues (except the plot colors that can be easily adjusted in the final version).

Reviewer comment (Novelty):
It cites a single probing paper on probing numeracy in embeddings, which is a highly relevant topic here, but many more exist (e.g., Naik et al. 2019, Sundararaman et al. 2020).

Author response:
Hereafter we reply to your specific Questions. Please also have a look at the global responses where we summarize our general comments. 

Q1: Thank you very much for the detailed review and the relevant references provided. As explained in the general comments a new version has been uploaded where the work is better framed into the interpretability literature.

Moreover, we fixed the minor presentation issues (except the plot colors that can be easily adjusted in the final version).

Reviewer comment (Novelty):
It cites no work on probing, many of which have discussed techniques similar to those presented here. For example, manipulating datasets on which a model is trained/evaluated to analyse its computational strategy (e.g., Linzen et al. 2016, Warstadt et al. 2020) and computing distances in embedding space which are representative of other notions of distances (e.g., Hewitt and Manning. 2019, White et al. 2021, Limisiewicz and Mareček. 2021).

Author response:
Hereafter we reply to your specific Questions. Please also have a look at the global responses where we summarize our general comments. 

Q1: Thank you very much for the detailed review and the relevant references provided. As explained in the general comments a new version has been uploaded where the work is better framed into the interpretability literature.

Moreover, we fixed the minor presentation issues (except the plot colors that can be easily adjusted in the final version).

Reviewer comment (Novelty):
It cites no related mechanistic interpretation work either. Hanna et al. (2023), in particular, study how GPT models implement a greater than operation, which is quite related to the research question investigated here.

Author response:
Hereafter we reply to your specific Questions. Please also have a look at the global responses where we summarize our general comments. 

Q1: Thank you very much for the detailed review and the relevant references provided. As explained in the general comments a new version has been uploaded where the work is better framed into the interpretability literature.

Moreover, we fixed the minor presentation issues (except the plot colors that can be easily adjusted in the final version).

Reviewer comment (Novelty):
The set of experiments already in this paper are a nice start on the quest to understand how arithmetic operations are implemented in language models, but this paper’s contributions could be much stronger if the authors engaged with the literature above and applied some of the more recent probing/interpretability techniques in their analyses. As is, the paper’s contributions are not strong enough to warrant publication in ICLR.

Author response:
Hereafter we reply to your specific Questions. Please also have a look at the global responses where we summarize our general comments. 

Q1: Thank you very much for the detailed review and the relevant references provided. As explained in the general comments a new version has been uploaded where the work is better framed into the interpretability literature.

Moreover, we fixed the minor presentation issues (except the plot colors that can be easily adjusted in the final version).