# PARAN: Persona-Augmented Review ANswering system on Food Delivery Review Dataset

arXiv: https://arxiv.org/abs/2512.10148 · HTML: https://mlsanity.com/paper/2512.10148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://mlsanity.com/paper/1902.00098/) (2019)
- [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018)
- [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://mlsanity.com/paper/2004.05388/) (2020)
- [Comparison of Diverse Decoding Methods from Conditional Language Models](https://mlsanity.com/paper/1906.06362/) (2019)
- [Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models](https://mlsanity.com/paper/1701.03185/) (2017)
- [Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints](https://mlsanity.com/paper/1809.01215/) (2018)
- [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017)
- [Automatic Generation of Personalized Comment Based on User Profile](https://mlsanity.com/paper/1907.10371/) (2019)
- [Automating App Review Response Generation](https://mlsanity.com/paper/2002.03552/) (2020)
- [Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation](https://mlsanity.com/paper/2102.10780/) (2021)
- [Self-Improving Customer Review Response Generation Based on LLMs](https://mlsanity.com/paper/2405.03845/) (2024)

## Abstract (arXiv)

Personalized review response generation presents a significant challenge in domains where user information is limited, such as food delivery platforms. While large language models (LLMs) offer powerful text generation capabilities, they often produce generic responses when lacking contextual user data, reducing engagement and effectiveness. In this work, we propose a two-stage prompting framework that infers both explicit (e.g., user-stated preferences) and implicit (e.g., demographic or stylistic cues) personas directly from short review texts. These inferred persona attributes are then incorporated into the response generation prompt to produce user-tailored replies. To encourage diverse yet faithful generations, we adjust decoding temperature during inference. We evaluate our method using a real-world dataset collected from a Korean food delivery app, and assess its impact on precision, diversity, and semantic consistency. Our findings highlight the effectiveness of persona-augmented prompting in enhancing the relevance and personalization of automated responses without requiring model fine-tuning.