论文标题
用概念一致性解开大型语言模型
Unpacking Large Language Models with Conceptual Consistency
论文作者
论文摘要
如果大型语言模型（LLM）对问题“是”回答“是高山？”那知道什么是山吗？您能否依靠它正确或错误地回应有关山脉的其他问题？大语言模型（LLMS）的成功表明，它们越来越能够准确地回答此类查询，但是该功能并不一定意味着对与锚质查询相关的概念的一般理解。我们提出概念上的一致性来衡量LLM对相关概念的理解。这个新颖的指标可以通过发现其对概念相关背景知识的查询的响应的一致性来衡量模型的表征。为了计算它，我们通过在知识库中遍历概念之间的路径来提取背景知识，然后尝试从背景知识中预测模型对锚点查询的响应。我们使用CSQA数据集和概念网知识库研究了当前LLM的性能。尽管与其他指标一样，概念一致性确实随所使用的LLM的规模而增加，但我们发现流行模型不一定具有很高的概念一致性。我们的分析还显示了各种关系，概念和提示之间概念一致性的显着差异。这是迈向建立模型的一步，即人类可以将心理理论应用于直觉上的互动。
If a Large Language Model (LLM) answers "yes" to the question "Are mountains tall?" then does it know what a mountain is? Can you rely on it responding correctly or incorrectly to other questions about mountains? The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query. We propose conceptual consistency to measure a LLM's understanding of relevant concepts. This novel metric measures how well a model can be characterized by finding out how consistent its responses to queries about conceptually relevant background knowledge are. To compute it we extract background knowledge by traversing paths between concepts in a knowledge base and then try to predict the model's response to the anchor query from the background knowledge. We investigate the performance of current LLMs in a commonsense reasoning setting using the CSQA dataset and the ConceptNet knowledge base. While conceptual consistency, like other metrics, does increase with the scale of the LLM used, we find that popular models do not necessarily have high conceptual consistency. Our analysis also shows significant variation in conceptual consistency across different kinds of relations, concepts, and prompts. This serves as a step toward building models that humans can apply a theory of mind to, and thus interact with intuitively.
