Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving

• URL: http://arxiv.org/abs/2501.02348v1
• Date: Sat, 04 Jan 2025 18:04:47 GMT
• Title: Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving
• Authors: Sanghyun Park, Boris Maciejovsky, Phanish Puranam,
• Abstract summary: Complex problem-solving requires the capacity to entertain multiple perspectives while preserving their distinctiveness.<n>We propose synthetic deliberation, a method that simulates discourse between agents embodying diverse perspectives.<n>This approach shows promise for strategic planning, policymaking, and conflict resolution.
• Score: 2.1175632266708733
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Complex problem-solving requires cognitive flexibility--the capacity to entertain multiple perspectives while preserving their distinctiveness. This flexibility replicates the "wisdom of crowds" within a single individual, allowing them to "think with many minds." While mental simulation enables imagined deliberation, cognitive constraints limit its effectiveness. We propose synthetic deliberation, a Large Language Model (LLM)-based method that simulates discourse between agents embodying diverse perspectives, as a solution. Using a custom GPT-based model, we showcase its benefits: concurrent processing of multiple viewpoints without cognitive degradation, parallel exploration of perspectives, and precise control over viewpoint synthesis. By externalizing the deliberative process and distributing cognitive labor between parallel search and integration, synthetic deliberation transcends mental simulation's limitations. This approach shows promise for strategic planning, policymaking, and conflict resolution.

Related papers

• VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search [89.43196232124883]
  VisuoThink is a novel framework that seamlessly integrates visuospatial and linguistic domains. It enables progressive visual-textual reasoning and incorporates test-time scaling through look-ahead tree search.
  arXiv  Detail & Related papers  (2025-04-12T08:37:30Z)
• Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs [86.79757571440082]
  Large language models (LLMs) such as OpenAI's o1 have demonstrated remarkable abilities in complex reasoning tasks. We identify a phenomenon we term underthinking, where o1-like LLMs frequently switch between different reasoning thoughts. We propose a decoding strategy with thought switching penalty TIP that discourages premature transitions between thoughts.
  arXiv  Detail & Related papers  (2025-01-30T18:58:18Z)
• A Cognitive Paradigm Approach to Probe the Perception-Reasoning Interface in VLMs [3.2228025627337864]
  This paper introduces a structured evaluation framework using Bongard Problems (BPs) to dissect the perception-reasoning interface in Vision-Language Models (VLMs) We propose three distinct evaluation paradigms, mirroring human problem-solving strategies. Our framework provides a valuable diagnostic tool, highlighting the need to enhance visual processing fidelity for achieving more robust and human-like visual intelligence in AI.
  arXiv  Detail & Related papers  (2025-01-23T12:42:42Z)
• Imagine while Reasoning in Space: Multimodal Visualization-of-Thought [70.74453180101365]
  Chain-of-Thought (CoT) prompting has proven highly effective for enhancing complex reasoning in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) We propose a new reasoning paradigm, Multimodal Visualization-of-Thought (MVoT) It enables visual thinking in MLLMs by generating image visualizations of their reasoning traces.
  arXiv  Detail & Related papers  (2025-01-13T18:23:57Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
  We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models. We aim for the models to engage with and generate responses that span a wider contextual scene understanding. Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
  arXiv  Detail & Related papers  (2024-03-20T11:27:20Z)
• Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation [42.472954457731355]
  We introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube.
  arXiv  Detail & Related papers  (2023-11-07T12:30:36Z)
• Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration [116.09561564489799]
  Solo Performance Prompting transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist is an intelligent agent that collaboratively combines multiple minds' strengths and knowledge to enhance problem-solving in complex tasks. Our in-depth analysis shows that assigning multiple fine-grained personas in LLMs improves problem-solving abilities compared to using a single or fixed number of personas.
  arXiv  Detail & Related papers  (2023-07-11T14:45:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.