自然语言推理的语义抽象：一种发现并补偿大型语言模型中语义知识与推理缺口的方法论框架
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
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中文总结 AI 辅助
针对LLM在自然语言推理中的语义知识缺口，提出基于语义兼容性/不兼容性的抽象框架，通过重构词汇语义关系提升准确率超10%，强调结构化知识而非数据规模。
中文摘要 AI 辅助
尽管大型语言模型（LLMs）在许多自然语言处理任务上表现出色，但它们在语义抽象方面仍面临严峻挑战。在本研究中，我们关注于理解LLMs如何在自然语言推理（NLI）中利用抽象语义知识，这需要复杂的语言能力来解释隐含意义、上下文概念关系以及词语和短语之间的语义联系。为此，我们提出了一种方法论框架，用于在更高的抽象层次上构建新的语义知识，我们将其定义为NLI中的语义兼容性和不兼容性概念。在该框架中，前提和假设之间的词汇-语义关系的意义被重新配置，以实现更灵活的语义网络，从而在LLMs中诱导不同的推理路径。这些新路径显示出一致的反应模式，使得能够就单一响应达成一致。结果表明，我们的提议能够发现并补偿LLMs在NLI中的语义知识缺口，在准确性方面取得了显著提升，某些模型提升超过10%，特别是对于非蕴含类别。必须指出，LLMs需要结构化知识而不仅仅是更多数据来弥合推理缺口。我们的混合方法将注意力引向被忽视的词语关系，使模型能够综合缺失信息。我们相信，未来不在于增加模型规模，而在于创建一种模拟人类思维灵活性的语义脚手架。希望我们的提议能够促进更强大的智能体和可解释推理的发展，引导AI走向可靠的语言理解。
英文摘要
Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested in understanding how LLMs leverage abstract semantic knowledge in natural language inference (NLI), which requires sophisticated linguistic capabilities to interpret implicit meanings, contextual conceptual relationships, and semantic connections between words and phrases. To this end, we propose a methodological framework for constructing new semantic knowledge at a higher level of abstraction, which we define under the notions of semantic compatibility and incompatibility for NLI. In this framework, the meaning of the lexical-semantic relations between the premise and the hypothesis is reconfigured to achieve a more flexible semantic network that induces different reasoning paths in LLMs. These new pathways show a consistent pattern of responses that allows agreement on a single response. The results demonstrate that our proposal allows to discover and compensate for LLMs' semantic knowledge gaps in NLI, achieving significant improvements in accuracy, exceeding 10% for some models, and in particular for the non-entailment class. It is essential to note that LLMs need structured knowledge and not just more data to bridge reasoning gaps. Our hybrid approach directs attention to overlooked word relationships, allowing models to synthesize missing information. We believe that the future lies not in increasing model size, but in creating a semantic scafolding that mimics the flexibility of human thinking. Hopefully, our proposal will enable the development of more robust agents and interpretable reasoning, guiding AI toward reliable language understanding.
发表机构
- Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos（莫雷洛斯州自治大学科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
