# Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art

arXiv: https://arxiv.org/abs/2607.29066 · HTML: https://mlsanity.com/paper/2607.29066/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 31 Jul 2026.

## Builds on (most cited first, 7 of 7)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method](https://mlsanity.com/paper/2310.00305/) (2023)
- [Toward Verifiable Misinformation Detection: A Multi-Tool LLM Agent Framework](https://mlsanity.com/paper/2508.03092/) (2025)
- [Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning](https://mlsanity.com/paper/2402.11432/) (2024)
- [Deception detection in text and its relation to the cultural dimension of individualism/collectivism](https://mlsanity.com/paper/2105.12530/) (2021)

## Abstract (arXiv)

Deception detection has critical implications for legal proceedings, law enforcement, and online security. Although human judgment is limited in accuracy and scalability, Natural Language Processing (NLP) offers a data-driven alternative. We present a survey and comparative analysis of NLP-based Automatic Deception Detection (ADD) focusing on the legal domain, reviewing the evolution from feature-based machine learning to Large Language Model (LLM) approaches. We conduct a unified empirical evaluation across seven datasets (two legal, five general-domain), comparing six fine-tuned transformer models and seven LLMs under four prompting strategies. The results show strong domain sensitivity, with fine-tuned models excelling in data-rich general domains and few-shot LLMs remaining competitive in low-resource legal settings. Chain-of-Thought prompting often underperforms direct classification. These findings highlight the need for domain adaptation and interpretable systems in high-stakes legal contexts.