Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still underperform on queries containing negation. To understand this phenomenon, we study negation in both traditional neural information retrieval and LLM-based models. We (1) introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions; (2) generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models and to fine-tune models for a more robust performance on negation; and (3) propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets. Our taxonomy produces a balanced data distribution over negation types, providing a better training setup that leads to faster convergence on the NevIR dataset. Moreover, we propose a classification schema that reveals the coverage of negation types in existing datasets, offering insights into the factors that might affect the generalization of fine-tuned models on negation.

翻译：理解并解决复杂推理任务对于满足用户信息需求至关重要。尽管稠密神经模型能够学习上下文嵌入表示，但在处理包含否定的查询时仍表现欠佳。为探究此现象，本研究系统考察了传统神经信息检索模型与基于大语言模型的否定处理能力。我们（1）构建了源自哲学、语言学与逻辑学定义的否定分类体系；（2）生成两个基准数据集，可用于评估神经信息检索模型性能，并对模型进行微调以提升其否定处理鲁棒性；（3）提出基于逻辑的分类机制，用于分析检索模型在现有数据集上的表现。本分类体系在否定类型上产生均衡的数据分布，通过优化训练配置使模型在NevIR数据集上实现更快收敛。此外，我们提出的分类框架揭示了现有数据集对否定类型的覆盖范围，为理解影响微调模型否定泛化能力的潜在因素提供了新见解。