ArticleScientific reports2024
Finding deceivers in social context with large language models and how to find them: the case of the Mafia game.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Comparing AI and human decision-making mechanisms in daily collaborative experiments.iScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lies are ubiquitous and often happen in social interactions. However, socially conducted deceptions make it hard to get data since people are unlikely to self-report their intentional deception behaviors, especially malicious ones. Social deduction games, a type of social game where deception is a key gameplay mechanic, can be a good alternative to studying social deceptions. Hence, we utilized large language models' (LLMs) high performance in solving complex scenarios that require reasoning and prompt engineering to detect deceivers in the game of Mafia given only partial information and found such an approach acquired better accuracy than previous BERT-based methods in human data and even surpassed human accuracy. Furthermore, we conducted extensive experiments and analyses to find out the strategies behind LLM's reasoning process so that humans could understand the gist of LLM's strategy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.