# Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM

arXiv: https://arxiv.org/abs/2409.07871 · HTML: https://mlsanity.com/paper/2409.07871/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2024.

## Built on by (most cited first, 16 of 16)

- [Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications](https://mlsanity.com/paper/2411.06837/) (2024)
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- [When Autonomy Breaks: The Hidden Existential Risk of AI](https://mlsanity.com/paper/2503.22151/) (2025)
- [Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law](https://mlsanity.com/paper/2601.05879/) (2026)
- [Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmas](https://mlsanity.com/paper/2602.04456/) (2026)
- [When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance](https://mlsanity.com/paper/2605.22975/) (2026)
- [Humans learn to prefer trustworthy AI over human partners](https://mlsanity.com/paper/2507.13524/) (2025)
- ["Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems](https://mlsanity.com/paper/2602.21127/) (2026)
- [Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI](https://mlsanity.com/paper/2604.17270/) (2026)
- [Implicit Humanization in Everyday LLM Moral Judgments](https://mlsanity.com/paper/2604.22764/) (2026)
- [Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines](https://mlsanity.com/paper/2607.05680/) (2026)
- [Toward Human Rights Benchmarking for LLMs: A Pilot Methodology](https://mlsanity.com/paper/2608.10268/) (2026)
- [Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making](https://mlsanity.com/paper/2609.24644/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N = 288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. The result of the source unknown condition was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work.