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arxiv: 2502.16810 · v6 · submitted 2025-02-24 · 💻 cs.AI · cs.CL· cs.HC· econ.GN· q-fin.EC

AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting

Jibang Wu , Chenghao Yang , Yi Wu , Simon Mahns , Chaoqi Wang , Hao Zhu , Fei Fang , Haifeng Xu This is my paper

Pith reviewed 2026-05-23 02:53 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.HCecon.GNq-fin.EC
keywords real estate marketingpersuasive language generationLLM agentscopywriting automationgrounded generationhuman preference evaluationfactual accuracypersonalization
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The pith

AI agent generates real estate marketing copy that buyers prefer over human expert writing while matching factual accuracy.

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

The paper develops an agentic framework using large language models for grounded persuasive language generation in automated copywriting, focused on real estate. It employs three modules that predict marketable features, align output with buyer preferences, and enforce factual accuracy plus localized details. Systematic experiments with a focus group of potential house buyers show the AI descriptions are preferred over human expert baselines by a clear margin. A sympathetic reader would care because the result indicates a scalable way to produce targeted marketing content without losing appeal or truthfulness.

Core claim

This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.

What carries the argument

Three-module agentic framework with Grounding Module to predict marketable features, Personalization Module to align with user preferences, and Marketing Module to ensure factual accuracy and localized features.

Load-bearing premise

The focus group of potential house buyers provides a representative sample of real-world preferences and the human expert baselines are a fair and unbiased comparison point.

What would settle it

A follow-up study with a larger or demographically broader buyer sample that rates the human-written descriptions as equal or superior, or that uncovers factual errors in the AI outputs.

Figures

Figures reproduced from arXiv: 2502.16810 by Chaoqi Wang, Chenghao Yang, Fei Fang, Haifeng Xu, Hao Zhu, Jibang Wu, Simon Mahns, Yi Wu.

Figure 1
Figure 1. Figure 1: Illustration of the Design Pipeline of AI Realtor. 4 THE AGENTIC IMPLEMENTATION OF AI RE A L T O R This section outlines the core design of AI Realtor, an AI agent that process multiple levels of marketing information to compose persuasive descriptions for real estate listings and actively learn to adapt its language to individual buyer preferences. At a high level, our approach operationalizes microeconom… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of the inductive feature schema construction pipeline. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of model performance using Elo ratings and win rates. Elo ratings represent [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Analyses of Simulating Human Feedback with AI Feedback. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Faithfulness Scores for Hallucination Checks. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Survey Screening Interface and ask for their ratings of importance on a 1-5 scale. We showcases the web user interfaces in [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Human Evaluation Interface B.4 FEATURE ANNOTATION INTERFACE To ease the task of feature annotation, we also develop a user-friendly web interface. Its design is shown in [PITH_FULL_IMAGE:figures/full_fig_p021_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Annotation Interface C IMPLEMENTATION DETAILS In this section, we provide a full description of the implementation detail of AI Realtor. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Credibility Scores for Hallucination Checks. [PITH_FULL_IMAGE:figures/full_fig_p030_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Interfaces used in the hallucination checks. [PITH_FULL_IMAGE:figures/full_fig_p031_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Interfaces used in the hallucination checks. [PITH_FULL_IMAGE:figures/full_fig_p032_12.png] view at source ↗
read the original abstract

This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing as a focal application. Our method is designed to align the generated content with user preferences while highlighting useful factual attributes. This agent consists of three key modules: (1) Grounding Module, mimicking expert human behavior to predict marketable features; (2) Personalization Module, aligning content with user preferences; (3) Marketing Module, ensuring factual accuracy and the inclusion of localized features. We conduct systematic human-subject experiments in the domain of real estate marketing, with a focus group of potential house buyers. The results demonstrate that marketing descriptions generated by our approach are preferred over those written by human experts by a clear margin while maintaining the same level of factual accuracy. Our findings suggest a promising agentic approach to automate large-scale targeted copywriting while ensuring factuality of content generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces an agentic LLM-based framework for grounded persuasive language generation in automated copywriting, focused on real estate marketing. It consists of three modules—Grounding (to predict marketable features), Personalization (to align with user preferences), and Marketing (to ensure factual accuracy and localized features)—and reports human-subject experiments with a focus group of potential house buyers claiming that the generated descriptions are preferred over those written by human experts by a clear margin while maintaining equivalent factual accuracy.

Significance. If the empirical results hold under rigorous controls, the work would demonstrate a practical agentic approach to scalable, targeted copywriting that integrates factuality constraints with personalization, offering a template for similar applications in other marketing domains.

major comments (1)
  1. [Abstract] Abstract: the headline claim of a 'clear margin' preference for AI-generated descriptions over human experts at matched factual accuracy is presented without any reported sample size, statistical tests, blinding procedures, rater demographics, recruitment criteria, or protocol for verifying factual accuracy, rendering the central empirical result unverifiable from the provided information.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful review and constructive comment on the abstract. We address the point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of a 'clear margin' preference for AI-generated descriptions over human experts at matched factual accuracy is presented without any reported sample size, statistical tests, blinding procedures, rater demographics, recruitment criteria, or protocol for verifying factual accuracy, rendering the central empirical result unverifiable from the provided information.

    Authors: We agree that the abstract as written does not include these methodological details, which are reported in Section 4 of the full manuscript. To improve verifiability at the abstract level, we will revise the abstract to incorporate key experimental parameters (e.g., participant count, preference margin with statistical support, and confirmation of factual accuracy protocol) while preserving length constraints. revision: yes

Circularity Check

0 steps flagged

Empirical human-evaluation study with no derivation chain or fitted predictions

full rationale

The paper presents an agentic framework with three descriptive modules (Grounding, Personalization, Marketing) and reports results from human-subject experiments on real-estate copywriting. No equations, first-principles derivations, parameter-fitting procedures, or predictions are described in the abstract or provided text. Central claims rest on external human preference and accuracy judgments rather than any internal reduction, self-definition, or self-citation chain. This is a standard empirical study whose validity can be assessed against the reported experimental protocol; no load-bearing step reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on domain assumptions about LLM capabilities rather than new mathematical constructs; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Large language models can be prompted to mimic expert human behavior in predicting marketable features.
    Underpins the Grounding Module as described in the abstract.
  • domain assumption Generated content can be aligned with user preferences via a dedicated personalization step while preserving factual accuracy.
    Central to the Personalization and Marketing Modules.

pith-pipeline@v0.9.0 · 5720 in / 1241 out tokens · 57686 ms · 2026-05-23T02:53:52.400951+00:00 · methodology

discussion (0)

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