Echoes of Humanity: The Collective Mind of LLM Agents

Engineering notes

Engineering notes will be added by the aipentium editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

The objective of this exploratory pilot study was to investigate the preliminary effectiveness of the wisdom of crowds approach in the context of AI agents based on Large Language Models (LLMs) to improve fraud detection in emails. To this end, a customized digital platform was developed to collect assessments through which responses from 122 human agents and 505 autonomous agents regarding the likelihood that emails were fraudulent were gathered. Diversity among the autonomous agents was introduced by varying the LLMs’ temperature parameter, which influences the level of randomness and propensity for hallucinations. Individual assessments were aggregated to compare collective versus individual performance. The initial results suggest that, within this proof-of-concept, aggregating the evaluation of a collective of LLM agents, even when individual agents exhibited a tendency toward hallucination, has the potential to lead to enhanced and more robust fraud detection, mitigating individual errors and leveraging distributed knowledge. Although many AI agents displayed a somewhat “paranoid” bias, suggesting the inheritance of human behavioral patterns, the AI “collective” achieved a notably strong performance, offering better predictions than individual agents in almost all cases.

Links and sources

Comments