Summary of Mindscope: Exploring Cognitive Biases in Large Language Models Through Multi-agent Systems, by Zhentao Xie et al.
MindScope: Exploring cognitive biases in large language models through Multi-Agent Systems
by Zhentao Xie, Jiabao Zhao, Yilei Wang, Jinxin Shi, Yanhong Bai, Xingjiao Wu, Liang He
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to detecting cognitive biases in large language models (LLMs) by introducing the ‘MindScope’ dataset, which integrates static and dynamic elements. The dataset consists of 5,170 open-ended questions covering 72 cognitive bias categories, as well as a rule-based multi-agent communication framework for generating dialogues. A multi-agent detection method is also introduced, combining Retrieval-Augmented Generation (RAG), competitive debate, and reinforcement learning to improve detection accuracy by up to 35.10% compared to GPT-4. This research has significant implications for the development of more accurate and reliable language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to detect cognitive biases in large language models. It creates a special dataset with many questions about different types of bias, and also makes a new way to test these models that combines several ideas from artificial intelligence. This helps make language models better at understanding what people mean when they write or speak. |
Keywords
» Artificial intelligence » Gpt » Rag » Reinforcement learning » Retrieval augmented generation
