# VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity

arXiv: https://arxiv.org/abs/2501.03139 · HTML: https://mlsanity.com/paper/2501.03139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 14 of 14)

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- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models](https://mlsanity.com/paper/2105.13318/) (2021)
- [User Modeling for Task Oriented Dialogues](https://mlsanity.com/paper/1811.04369/) (2018)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [In-Context Learning User Simulators for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2306.00774/) (2023)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [Adversarial Text Generation Without Reinforcement Learning](https://mlsanity.com/paper/1810.06640/) (2018)
- [Discovering the Hidden Facts of User-Dispatcher Interactions via Text-based Reporting Systems for Community Safety](https://mlsanity.com/paper/2211.04618/) (2022)

## Abstract (arXiv)

Scenario-based training has been widely adopted in many public service sectors. Recent advancements in Large Language Models (LLMs) have shown promise in simulating diverse personas to create these training scenarios. However, little is known about how LLMs can be developed to simulate victims for scenario-based training purposes. In this paper, we introduce VicSim (victim simulator), a novel model that addresses three key dimensions of user simulation: informational faithfulness, emotional dynamics, and language style (e.g., grammar usage). We pioneer the integration of scenario-based victim modeling with GAN-based training workflow and key-information-based prompting, aiming to enhance the realism of simulated victims. Our adversarial training approach teaches the discriminator to recognize grammar and emotional cues as reliable indicators of synthetic content. According to evaluations by human raters, the VicSim model outperforms GPT-4 in terms of human-likeness.