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OmniToM：通过显式信念建模评估大型语言模型中的心智理论OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
OmniToM的引入标志着在评估大型语言模型（LLMs）心智理论（ToM）能力方面的重大进展。这个新基准专注于叙事中的显式信念建模，使研究人员能够评估LLMs在社会推理背景下理解和表示不同角色信念的能力。初步发现表明，当前的LLMs在准确跟踪和表示这些信念方面存在困难，突显了它们在社会推理能力上的关键局限性。OmniToM旨在为该领域的进一步研究和改进提供一个结构化框架。
The introduction of OmniToM marks a significant advancement in the evaluation of Theory of Mind (ToM) capabilities in large language models (LLMs). This new benchmark focuses on explicit belief modeling within narratives, allowing researchers to assess how well LLMs can understand and represent the beliefs of different characters in social reasoning contexts. Initial findings indicate that current LLMs struggle with accurately tracking and representing these beliefs, highlighting critical limitations in their social reasoning abilities. OmniToM aims to provide a structured framework for further research and improvement in this area.
要点
OmniToM是评估LLMs中心智理论的新基准。
OmniToM is a new benchmark for assessing Theory of Mind in LLMs.
当前的LLMs在理解和表示叙事中的信念方面存在局限性。
Current LLMs exhibit limitations in understanding and representing beliefs in narratives.
该基准旨在指导未来研究，以增强LLMs的社会推理能力。
The benchmark aims to guide future research in enhancing social reasoning capabilities of LLMs.
为何重要
理解AI中的心智理论对于开发能够更自然和有效地与人类互动的系统至关重要。这个基准可能会导致AI理解社会动态的显著改善。
Understanding Theory of Mind in AI is crucial for developing systems that can interact more naturally and effectively with humans. This benchmark could lead to significant improvements in how AI understands social dynamics.
产品经理
对于产品经理而言，OmniToM提供了对LLMs当前局限性的洞察，这可以为专注于增强用户互动和社会推理能力的产品开发策略提供信息。
For product managers, OmniToM provides insights into the current limitations of LLMs, which can inform product development strategies focused on enhancing user interactions and social reasoning capabilities.
开发者
开发人员可以利用OmniToM的发现来优化LLM架构和训练方法，最终导致更复杂的AI系统，更好地理解类人推理。
Developers can leverage the findings from OmniToM to refine LLM architectures and training methods, ultimately leading to more sophisticated AI systems that better understand human-like reasoning.
学生与求职者
在AI领域的学生和求职者可以通过理解OmniToM所突显的挑战受益，因为这代表了一个不断增长的研究领域，可能会带来新的AI开发和应用机会。
Students and job-seekers in AI can benefit from understanding the challenges highlighted by OmniToM, as it represents a growing area of research that could lead to new opportunities in AI development and application.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.AI
OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief ModelingResearchers introduced OmniToM, a benchmark aimed at evaluating Theory of Mind (ToM) in large language models (LLMs) by modeling belief structures in narratives. The benchmark reveals current limitations in LLMs' ability to track and represent the beliefs of various actors in social reasoning scenarios.