We appreciate the reviewer's responsiveness.
### Novelty
We first emphasize that our main contribution is to propose the idea of combining RAG and ad auctions for LLM and laying down a foundation in this line of research. Indeed, even though applying RAG into the LLM ad auction seems a very plausible direction, there has been no single work dedicated to it. That is to say, our primary objective and contribution is to propose a potentially promising framework for the LLM ad auctions, which we believe will inevitably emerge in a near future, and validate its feasibility.
Although RAG was introduced in 2020 and advertising has long been a fascinating field of study, the integration of ads within the RAG framework (to potentially monetize LLMs) is a novel approach that was not studied theoretically nor validated through empirical research.
As the reviewer agreed with the idea of perturbation, although this idea is easily understandable in hindsight, it is not easy to come up with at first glance. Also, we think that the idea of reverse-engineering the mechanism design by starting from an exogenously given allocation function by RAG is not a trivial perspective to derive. We respectfully disagree with the reviewer that a paper necessarily includes complicated theoretical techniques or proofs, but believe that simple and concise but novel ideas often advance the fields in many directions.
Finally, as the reviewer pointed out, delving more into logarithmic social welfare seems very promising research directions. We indeed have exposited several initial properties in Appendix D including that it can be viewed as a version of weighted Nash social welfare, but further theoretical results remains open problems, e.g., whether this is consistent with other notions of fairness in terms of reachability/approximability. We will make this point clearer in the main body of the paper.
### Experiments with longer segments
We would like to emphasize that optimizing the incorporation of ads into the LLM output, via prompt engineering, is not a goal of this paper. Our intention in the experiments was to provide a proof of concept with basic, straightforward prompts, to confirm that the approach was viable (and surely in practice we may apply this approach for longer answers etc for quality purposes).
Having said that below we bring another sample that we obtained using the single ad segment auction without replacement, even without much of prompt engineering (we just reused the original prompts but slightly added a bit: keep answering the query without deviating from it by fully focusing on the ads). We conducted this experiment several times, and found that LLM has been pretty consistent on almost all of them, i.e., a rather simple prompting structure would work.
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We can definitely include these samples in the appendix for the camera ready if the reviewer finds it informative.