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Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM

Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM
Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM
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. This result 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, and the importance of language complexity and real-world comparability.
Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Schneiders, Eike
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Seabrooke, Tina
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Krook, Joshua
e7261d11-4357-4e51-baca-115e64ae54dd
Hyde, Richard
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Leesakul, Natalie
953c9cf3-ac2f-4d8c-9ed7-da47f356626b
Clos, Jeremie
398fea21-4dc6-42ee-a2f1-cad9a789b378
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68
Schneiders, Eike
9da80af0-1e27-4454-90e2-eb1abf7108bd
Seabrooke, Tina
bf0d9ea5-8cf7-494b-9707-891762fce6c3
Krook, Joshua
e7261d11-4357-4e51-baca-115e64ae54dd
Hyde, Richard
f6713a3a-0130-4471-8b8c-a5d4907ef4e9
Leesakul, Natalie
953c9cf3-ac2f-4d8c-9ed7-da47f356626b
Clos, Jeremie
398fea21-4dc6-42ee-a2f1-cad9a789b378
Fischer, Joel
a320ad79-0fb5-464b-9eac-f74918b5ea68

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

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. This result 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, and the importance of language complexity and real-world comparability.

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In preparation date: 1 September 2024
Keywords: Computer Science - Human-Computer Interaction, Computer Science - Computers and Society

Identifiers

Local EPrints ID: 494609
URI: http://eprints.soton.ac.uk/id/eprint/494609
PURE UUID: 99de1e47-bce4-4752-b869-664acb02f14e
ORCID for Eike Schneiders: ORCID iD orcid.org/0000-0002-8372-1684
ORCID for Tina Seabrooke: ORCID iD orcid.org/0000-0002-4119-8389

Catalogue record

Date deposited: 10 Oct 2024 17:05
Last modified: 11 Oct 2024 02:11

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Contributors

Author: Eike Schneiders ORCID iD
Author: Tina Seabrooke ORCID iD
Author: Joshua Krook
Author: Richard Hyde
Author: Natalie Leesakul
Author: Jeremie Clos
Author: Joel Fischer

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