Browsing by Subject "Prompt engineering"

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Exploring multiple perspectives to mitigate cognitive biases through an integrated interface to language models
(2024-05) Wong, Yian ; Lease, Matthew A.; Li, Junyi Jessy
In recent years, large language models (LLMs) have demonstrated remarkable abilities in generating human-like text and supporting decision-making processes. However, their use is often limited by inherent biases and a lack of diversity in presented perspectives. This work introduces a novel system designed to mitigate these issues by leveraging the capabilities of LLMs to simulate a multi-perspective debate format, aimed at providing a balanced view on controversial topics. The proposed system employs a unique integrated interface that facilitates dynamic interactions between multiple AI-generated personas, each representing distinct viewpoints. These personas engage in structured debates, allowing for a comprehensive exploration of a topic that counteracts the cognitive biases typically associated with single-perspective information retrieval systems. The system incorporates advanced prompt engineering techniques and retrieval-augmented generation to ensure the accuracy and relevance of the information presented. Additionally, the interface is designed with user engagement in mind, featuring interactive elements that allow users to manipulate the debate dynamics and contribute to the discussion. This thesis evaluates the system’s effectiveness in enhancing users' understanding of complex issues and its potential in reducing bias in decision support systems. By simulating diverse viewpoints, the system potentially fosters more critical and informed engagement with topics, thus supporting better decision-making.