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REVIEW 5 major objections 6 minor 1 cited by

Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Rewriting ad text with a PPO-trained language model improves both retrieval rank and inclusion in LLM-generated answers, without retraining the retriever, reaching a DeltaDIR@5 of 2.79 and DeltaMRR@5 of 0.0073 under instruction-based…

desk verdict The central idea is sensible, but the paper's headline visibility metric excludes exactly the new top-K entries that matter, so the main empirical claim is not supported as stated. read the letter →

arxiv 2507.21099 v1 pith:C5E6G7I7 submitted 2025-07-03 cs.CL

Chloe Ho , Ishneet Sukhvinder Singh , Diya Sharma , Tanvi Reddy Anumandla , Michael Lu , Vasu Sharma , Kevin Zhu This is my paper
classification cs.CL
keywords advertisementrewritingretrieval-augmentedgenerationProximalPolicyOptimizationdocumentrankingcontentfidelityDeltaMRRDIRadvisibility
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that how an advertisement is phrased matters in retrieval-based LLM systems, and that a language model can be trained to rewrite ads so they rank higher and are cited more often in generated answers, all without modifying the retriever. The authors build a supervised fine-tuning loss that balances query relevance, distance from competing ads, and fidelity to the original text, then add Proximal Policy Optimization (PPO) using the negative of that loss as a reward. In instruction-based prompting, PPO outperforms both prompt engineering and supervised fine-tuning, lifting document-inclusion rate at top-5 by 2.79 and reciprocal rank by 0.0073. This matters because advertisers typically face black-box retrievers they cannot retrain, making content-side rewriting a practical lever for visibility.

What carries the argument

The load-bearing mechanism is a composite loss, $L_{\text{total}} = L_{\text{rel gain}} + L_{\text{triplet}} + L_{\text{fidelity}}$, which also serves as the PPO reward via $\text{reward} = -L_{\text{total}}$. $L_{\text{rel gain}} = -(\text{sim}(q, d_{\text{after}}) - \text{sim}(q, d_{\text{before}}))$ pushes the rewritten ad closer to the query; $L_{\text{triplet}} = -[\text{sim}(q, d_{\text{after}}) - \tfrac{1}{3}\sum_{n \in N} \text{sim}(n, q)]$ pushes it above the average similarity of three sampled top-k competitors; and $L_{\text{fidelity}} = 1 - \text{sim}(d_{\text{after}}, d_{\text{before}})$ anchors the rewrite to the original meaning. All similarities are cosine similarities under the all-MiniLM-L6-v2 embedding model, the same embedding used both to compute the reward during training and to rank documents at test time, so the training objective directly matches the evaluation metric.

What would settle it

Re-rank the same rewritten ads with a different retriever, for example a BM25 sparse retriever, a commercially deployed embedding model, or a learned ranker, without retraining the rewriter, and check DeltaMRR@5 and DeltaDIR@5; if the improvements collapse or turn negative, the claimed visibility gains are tied to optimizing the exact embedding used in evaluation rather than to generally better ad text.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a PPO-trained rewriter, rewarded by a composite loss of relevance gain, triplet margin against competing ads, and content fidelity, produces ad text that ranks higher and is included more often in an LLM's response than the original ad, prompt-engineered rewrites, or supervised fine-tuning. Under instruction-based prompting the method reaches its headline numbers: DeltaMRR@5 = 0.0073 and DeltaDIR@5 = 2.7944, both reported as statistically significant at a 95% confidence level. Under few-shot prompting PPO still improves inclusion (DeltaDIR@5 = 1.9267) but slightly lowers rank (DeltaMRR@5 = -0.0008), showing that inclusion and ranking can move apart. The paper further finds that equal weighting of the three loss terms is the most balanced configuration, and that rewriting effects peak around k = 5 to k = 10 and turn negative beyond k = 20.

Load-bearing premise

The whole pipeline is trained and evaluated on cosine similarity from the all-MiniLM-L6-v2 embedding model, so the gains assume that embedding is a faithful proxy for whatever retrieval system advertisers actually face.

Editorial extensions

If this is right

  • Advertisers can gain visibility by rewriting ad copy with an LLM rather than paying for ranking changes or waiting for retriever upgrades.
  • Because the retriever is left untouched, the approach applies to black-box retrieval systems where advertisers have no access to ranking internals.
  • Instruction-based prompting with chain-of-thought is the more reliable setting, yielding positive rank gains, while few-shot rewriting can improve inclusion at the cost of rank.
  • Rewriting helps most in the top-5 to top-10 window and becomes harmful beyond roughly k = 20, suggesting the method targets first-page visibility.
  • The loss-weight ablation gives practitioners a dial: equal weights for balanced gains, or inclusion-focused weights such as 45:35:20 when being cited matters more than rank.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural transferability test would evaluate the trained rewrites under a different embedding model or a learned ranker; if the gains vanish, the reported numbers are partly an artifact of optimizing and measuring with the same all-MiniLM-L6-v2 cosine similarity.
  • The DeltaDIR metric depends on one black-box LLM (Gemini 1.5 Pro), so extending evaluation to several LLMs would show whether inclusion gains reflect general answer quality or quirks of that model's prompt following.
  • If many advertisers adopt the same rewriter, the relative ranking benefit may become zero-sum, though absolute inclusion gains could persist if the LLM finds rewritten text genuinely more useful.
  • The same retriever-aware rewriting recipe could be applied to other visibility-sensitive documents such as product listings, job postings, or scientific abstracts, with the loss reweighted to each domain's retrieval stack.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes to improve ad visibility in retrieval-augmented LLM pipelines by rewriting ad text with a fine-tuned language model while keeping the retriever fixed. The method combines a supervised loss (relevance gain, triplet margin against top-k distractors, and fidelity to the original ad) with a PPO stage whose reward is the negative of that loss, and evaluates on 1,000 held-out ads from Microsoft's CommercialAdsDataset using an all-MiniLM-L6-v2 exact inner-product index and Gemini 1.5 Pro for inclusion measurement. The headline results are a DeltaMRR@5 of 0.0073 and a DeltaDIR@5 of 2.7944 for instruction-based prompting, which the abstract and Section 5.1 attribute to PPO outperforming prompt engineering and supervised fine-tuning.

Significance. If the results hold, the paper demonstrates a practical producer-side lever for ad visibility in RAG systems and introduces a reusable evaluation suite (DeltaMRR@K, DeltaDIR@K). The authors provide exact-search retrieval configuration, extensive ablations over k and loss weightings, and anonymous code repositories, which are strengths. However, the DeltaDIR metric is defined in a way that excludes the main visibility event (a document entering top-K), and the rank metric is computed under the same embedding model used as the training reward; both issues must be addressed before the central claim can be accepted.

major comments (5)
  1. [Section 4.1.2] DeltaDIR@K is computed only over the query set Q_d^(K) = {q | rank_before(d,q) <= K and rank_after(d,q) <= K}, which excludes every query in which rewriting lifts the ad from outside top-K into top-K, as well as every query where it drops out. Consequently, the headline DeltaDIR@5 = 2.7944 in Table 2 and the abstract's "2.79" do not measure the stated "inclusion frequency improvement" in the intended sense: moving an ad from rank 11 to rank 4 contributes nothing to DeltaDIR@5. The metric should be recomputed over all relevant queries (or at least include newly-entering queries), and the comparison with DeltaMRR should be made on the same query set.
  2. [Section 5.1] The claim that PPO's DeltaMRR and DeltaDIR values are "statistically significant under a 95% confidence interval" is unsupported: no confidence intervals, standard errors, or significance tests are reported in Section 5, the tables, or the appendix. This is material because the zero-rewrite baseline in Table 1 has DeltaDIR@5 = -0.0807 despite unchanged content, and Table 4 shows zero-rewrite DeltaDIR fluctuating between -0.558 and 0.6293 across k; the metric is noisy enough that error bars are essential for the central comparison.
  3. [Sections 4.3 and 5] The PPO reward is the negative of the composite loss whose dominant term is -(sim(q, dafter) - sim(q, dbefore)) under all-MiniLM-L6-v2, and the DeltaMRR@K evaluation in Section 5 is computed from rankings produced by the same all-MiniLM-L6-v2 embedding index (Section 3.2). The reported rank improvements are therefore partly circular: the model is rewarded for maximizing exactly the similarity function used to compute the evaluation ranking. To support the claim that rewriting transfers to real retrieval systems, the authors should evaluate with at least one held-out retriever or embedding model (or a learned ranker) and report the resulting DeltaMRR.
  4. [Section 3.3.2] The triplet loss is not well-defined as written. The text defines s¬(q) as the scalar average of the distractor similarities to the query, but then writes the loss as -[sim(q, dafter) - sim(q, s¬(q))], treating s¬(q) as a vector. Please restate the loss in a mathematically coherent form, for example by comparing sim(q, dafter) with the average of sim(q, n) over the distractor set Nq,d.
  5. [Abstract, Tables 2-3] The claim that PPO "outperforms both prompt engineering and supervised fine-tuning in most cases" is not supported by the few-shot condition: Table 3 shows PPO DeltaMRR@5 = -0.0008 (negative) and DeltaDIR@5 = 1.9267, which is close to prompt engineering's 1.9133. The central claim should be scoped to instruction-based prompting, or additional evidence should be provided to justify the "most cases" phrasing.
minor comments (6)
  1. [Section 3.2] The paper states that all-MiniLM-L6-v2 produces a "1384-dimensional output"; this model actually produces 384-dimensional embeddings. Please correct this factual error.
  2. [Section 4.3] The text says the reward is "the negative of the total loss introduced in Section 3.2", but the loss is introduced in Section 3.3. Please fix the cross-reference.
  3. [Section 3.1] The data construction section does not specify how many queries were generated per domain-subdomain pair, how queries were sampled, or how the top-k ads were selected; please provide these details for reproducibility.
  4. [Appendix A.1] The inclusion prompt instructs the model to "promoting one or two relevant products" and to "Include brand and url", which may artificially constrain the LLM's inclusion behavior; the paper should state the sampling temperature and discuss this potential bias.
  5. [Section 5.2.2] The text says the equal weighting (1:1:1) "outperforms most alternative weightings", but Table 11 shows that the 45:35:20 weighting achieves DeltaDIR@5 = 2.9368, which is higher than the equal-weighting 2.7944; please clarify that the claim concerns ranking consistency rather than raw DeltaDIR.
  6. [Section 6] The statement that PPO "surfaces high-IDF keywords valued by the retriever" is speculative: no lexical or feature analysis of the generated rewrites is provided. Please add such an analysis or soften the claim.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the visibility claim rests on an independent LLM-inclusion metric and held-out test ads, despite a shared embedding between reward and ranking metric.

full rationale

The paper's derivation chain is not circular. The PPO reward is the negative of a loss whose main terms maximize sim(q, dafter) under all-MiniLM-L6-v2, and DeltaMRR@K is computed from ranks produced by the same embedding, so the ranking result is partially aligned with the training objective. This is a limitation on external validity, not a circular reduction: the model is trained on 10,000 ads and evaluated on a held-out 1,000 ads; rank is a thresholded reciprocal transformation of similarity rather than the raw similarity; and the few-shot results (Table 3) show PPO DeltaMRR@5 = -0.0008, i.e., optimizing the reward did not force a positive ranking result. DeltaDIR@K is evaluated with a separate black-box LLM (Gemini 1.5 Pro) that is not part of the reward, so the headline visibility improvement has independent empirical content. The DeltaDIR@K definition (Section 4.1.2) restricts to queries where the ad is in top-K both before and after, which is a construct-validity concern (new entries into top-K are excluded), but it is not a circular derivation; the zero-rewrite baseline (Table 1) also shows metric noise. There are no load-bearing self-citations or imported uniqueness theorems. Overall, the central claim does not reduce to its inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the fidelity of all-MiniLM-L6-v2 cosine similarity as the ranking oracle, used in both training and evaluation, on Gemini 1.5 Pro as a proxy for real LLM inclusion behavior, and on an unreferenced Microsoft ad dataset. The loss weights and distractor count are hand-chosen; the highlighted peak weights are selected post hoc from the ablation grid.

free parameters (3)
  • Loss weights (alpha, beta, gamma) = 1:1:1 equal weighting; peak claims use 0.45/0.35/0.20 and 0.35/0.45/0.20
    The composite loss in Section 3.3 is defined with equal 1:1:1 weights; the ablation grid in Section 5.2.2 selects different weights post hoc to maximize DeltaDIR, making the peak results a fitted choice.
  • Distractor sample size (Nq,d) = 3
    Section 3.3.2 samples 3 documents from the top-k list; the number is chosen without sensitivity analysis.
  • Queries per ad in PPO reward = 3
    Section 4.3 identifies up to three domain-matched queries per ad to average the reward; the choice is ad hoc.
assumptions (4)
  • domain assumption Cosine similarity in all-MiniLM-L6-v2 embedding space determines retrieval relevance.
    Section 3.2 uses this embedding model for the FAISS index; the loss and evaluation both rely on it.
  • domain assumption Gemini 1.5 Pro with the Appendix prompt is a valid proxy for LLM inclusion of ads in real systems.
    Section 3 relies on it for DeltaDIR evaluation; no validation against user behavior is provided.
  • ad hoc to paper CommercialAdsDataset by Microsoft is a representative and accessible ad corpus.
    Section 3.1 references the dataset with no URL or documentation; reproducibility depends on it.
  • domain assumption Domain and subdomain labels are reliable, with 85% human verification.
    Section 3.1 says labels were verified with 85% accuracy, but the metric is undefined and the 15% error is unexamined.

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Cite this review

Pith. "Pith review of Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting." pith.science (2026). https://pith.science/paper/C5E6G7I7

@misc{pith2026250721099,
  author       = {Pith},
  title        = {Pith review of: Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C5E6G7I7}},
  note         = {Machine review of arXiv:2507.21099}
}
read the original abstract

Search algorithms and user query relevance have given LLMs the ability to return relevant information, but the effect of content phrasing on ad visibility remains underexplored. We investigate how LLM-based rewriting of advertisements can improve their ranking in retrieval systems and inclusion in generated LLM responses, without modifying the retrieval model itself. We introduce a supervised fine-tuning framework with a custom loss balancing semantic relevance and content fidelity. To evaluate effectiveness, we propose two metrics: DeltaMRR@K (ranking improvement) and DeltaDIR@K (inclusion frequency improvement). Our approach presents a scalable method to optimize ad phrasing, enhancing visibility in retrieval-based LLM workflows. Experiments across both instruction-based and few-shot prompting demonstrate that PPO trained models outperform both prompt engineering and supervised fine-tuning in most cases, achieving up to a 2.79 DeltaDIR@5 and 0.0073 DeltaMRR@5 in instruction-based prompting. These results highlight the importance of how the ad is written before retrieval and prompt format and reinforcement learning in effective ad rewriting for LLM integrated retrieval systems.

Figures

Figures reproduced from arXiv: 2507.21099 by the authors.

Figure 1
Figure 1. ∆MRR metric for instruction-based prompting across k values. 0 5 10 15 20 25 30 k 0.002 0.000 0.002 0.004 0.006 0.008 MRR@K Few-shot Prompting Model Prompt Eng. SFT PPO [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 3
Figure 3. ∆DIR metric for instruction-based prompting across k values. 0 5 10 15 20 25 30 k 1.0 0.5 0.0 0.5 1.0 1.5 2.0 DIR@K Few-shot Prompting Model Prompt Eng. SFT PPO [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. ∆MRR@k metric for instruction-based prompting across varying loss weights. 0 5 10 15 20 25 30 k 0.008 0.006 0.004 0.002 0.000 MRR@K Few-shot Prompting Loss Weights 1:1:1 35:45:20 45:35:20 45:20:35 20:45:35 30:60:10 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figures from the paper (1 more)
Figure 7
Figure 7. Figure 7: ∆DIR@k metric for instruction-based prompting across varying loss weights.. 0 5 10 15 20 25 30 k 1.0 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 DIR@K Few-shot Prompting Loss Weights 1:1:1 35:45:20 45:35:20 45:20:35 20:45:35 30:60:10 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Cited by 1 Pith paper

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  1. Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023-2026)

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