TY - GEN
T1 - Does GPT-4 pass the Turing test?
AU - Jones, Cameron R.
AU - Bergen, Benjamin K.
N1 - Publisher Copyright:
©2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%). Participants’ decisions were based mainly on linguistic style (35%) and socioemotional traits (27%), supporting the idea that intelligence, narrowly conceived, is not sufficient to pass the Turing test. Participant knowledge about LLMs and number of games played positively correlated with accuracy in detecting AI, suggesting learning and practice as possible strategies to mitigate deception. Despite known limitations as a test of intelligence, we argue that the Turing test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging humanlikeness.
AB - We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%). Participants’ decisions were based mainly on linguistic style (35%) and socioemotional traits (27%), supporting the idea that intelligence, narrowly conceived, is not sufficient to pass the Turing test. Participant knowledge about LLMs and number of games played positively correlated with accuracy in detecting AI, suggesting learning and practice as possible strategies to mitigate deception. Despite known limitations as a test of intelligence, we argue that the Turing test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging humanlikeness.
UR - https://www.scopus.com/pages/publications/85199524622
U2 - 10.18653/v1/2024.naacl-long.290
DO - 10.18653/v1/2024.naacl-long.290
M3 - Conference contribution
AN - SCOPUS:85199524622
T3 - Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024
SP - 5183
EP - 5210
BT - Long Papers
A2 - Duh, Kevin
A2 - Gomez, Helena
A2 - Bethard, Steven
PB - Association for Computational Linguistics (ACL)
T2 - 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024
Y2 - 16 June 2024 through 21 June 2024
ER -