# Garbage in, garbage out: Zero-shot detection of crime using Large Language Models

arXiv: https://arxiv.org/abs/2307.06844 · HTML: https://mlsanity.com/paper/2307.06844/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 9 of its 10 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)

## Builds on (most cited first, 9 of 9)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [Evaluating Commonsense in Pre-trained Language Models](https://mlsanity.com/paper/1911.11931/) (2019)

## Built on by (most cited first, 5 of 5)

- [Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey](https://mlsanity.com/paper/2404.00990/) (2024)
- [Legal Evalutions and Challenges of Large Language Models](https://mlsanity.com/paper/2411.10137/) (2024)
- [The Epistemological Consequences of Large Language Models: Rethinking collective intelligence and institutional knowledge](https://mlsanity.com/paper/2512.19570/) (2025)
- [Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity](https://mlsanity.com/paper/2307.10577/) (2023)
- [Can Foundation Models Reliably Identify Spatial Hazards? A Case Study on Curb Segmentation](https://mlsanity.com/paper/2406.07202/) (2024)

## Abstract (arXiv)

This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos. We show that when video is (manually) converted to high quality textual descriptions, large language models are capable of detecting and classifying crimes with state-of-the-art performance using only zero-shot reasoning. However, existing automated video-to-text approaches are unable to generate video descriptions of sufficient quality to support reasoning (garbage video descriptions into the large language model, garbage out).