The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions

arXiv — cs.CLThursday, November 13, 2025 at 5:00:00 AM
The study titled 'The Collective Turing Test' reveals that large language models (LLMs) like Llama 3 70B and GPT-4o can convincingly simulate human conversations, particularly in social media contexts. By analyzing authentic discussions from Reddit, researchers demonstrated that participants misidentified LLM-generated content as human-created 39% of the time, with Llama 3 showing a 56% identification rate. This finding underscores the dual-edged nature of LLMs: while they offer innovative avenues for simulating online interactions and testing content policies, they also pose significant risks regarding the generation of misleading or inauthentic content. The implications of this study are profound, as they call for careful consideration of how LLMs are deployed in digital spaces, emphasizing the need for ethical guidelines to prevent misuse.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Chinese toymaker FoloToy suspends sales of its GPT-4o-powered teddy bear, after researchers found the toy gave kids harmful responses, including sexual content (Brandon Vigliarolo/The Register)
NegativeArtificial Intelligence
Chinese toymaker FoloToy has suspended sales of its GPT-4o-powered teddy bear after researchers from PIRG discovered that the toy provided harmful responses to children, including sexual content. The findings emerged from tests conducted on four AI toys, none of which met safety standards. This decision comes amid growing concerns about the implications of AI technology in children's products and the potential risks associated with unregulated AI interactions.
Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkish
NeutralArtificial Intelligence
A recent study evaluates the performance of seven advanced large language models (LLMs) on low-resource and morphologically rich languages, specifically Cantonese, Japanese, and Turkish. The research highlights the models' effectiveness in tasks such as open-domain question answering, document summarization, translation, and culturally grounded dialogue. Despite impressive results in high-resource languages, the study indicates that the effectiveness of LLMs in these less-studied languages remains underexplored.
VP-Bench: A Comprehensive Benchmark for Visual Prompting in Multimodal Large Language Models
PositiveArtificial Intelligence
VP-Bench is a newly introduced benchmark designed to evaluate the ability of multimodal large language models (MLLMs) to interpret visual prompts (VPs) in images. This benchmark addresses a significant gap in existing evaluations, as no systematic assessment of MLLMs' effectiveness in recognizing VPs has been conducted. VP-Bench utilizes a two-stage evaluation framework, involving 30,000 visualized prompts across eight shapes and 355 attribute combinations, to assess MLLMs' capabilities in VP perception and utilization.
Semantic VLM Dataset for Safe Autonomous Driving
PositiveArtificial Intelligence
The CAR-Scenes dataset is a newly released frame-level dataset designed for autonomous driving, facilitating the training and evaluation of vision-language models (VLMs) for scene-level understanding. It comprises 5,192 images sourced from Argoverse 1, Cityscapes, KITTI, and nuScenes, annotated using a comprehensive 28-key category/sub-category knowledge base. The dataset includes over 350 attributes and employs a GPT-4o-assisted vision-language pipeline for annotation, ensuring high-quality data through human verification.
LLM-as-a-Grader: Practical Insights from Large Language Model for Short-Answer and Report Evaluation
NeutralArtificial Intelligence
A recent study published on arXiv investigates the use of Large Language Models (LLMs), specifically GPT-4o, for grading short-answer quizzes and project reports in an undergraduate Computational Linguistics course. The research involved approximately 50 students and 14 project teams, comparing LLM-generated scores with evaluations from teaching assistants. Results indicated a strong correlation (up to 0.98) with human graders and exact score agreement in 55% of quiz cases, highlighting both the potential and limitations of LLM-based grading systems.
Emotions, Context, and Substance Use in Adolescents: A Large Language Model Analysis of Reddit Posts
NeutralArtificial Intelligence
A recent study analyzed 23,000 substance-use related posts from Reddit's r/teenagers community between 2018 and 2022, focusing on the emotional and contextual factors influencing adolescent substance use. The analysis utilized large language models to annotate posts for six emotions: sadness, anger, joy, guilt, fear, and disgust. Findings revealed that negative emotions were more prevalent in substance-use posts, while joy was dominant in non-substance discussions. Peer influence emerged as a significant contextual factor in these discussions.