SKA -Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLM s (2025)

Abstract: Although large language models (LLMs) have made significant progress in understanding Structured Knowledge (SK) like KG and Table, existing evaluations for SK understanding are non-rigorous (i.e., lacking evaluations of specific capabilities) and focus on a single type of SK. Therefore, we aim to propose a more comprehensive and rigorous structured knowledge understanding benchmark to diagnose the shortcomings of LLMs. In this paper, we introduce SKA-Bench, a Structured Knowledge Augmented QA Benchmark that encompasses four widely used structured knowledge forms: KG, Table, KG+Text, and Table+Text. We utilize a three-stage pipeline to construct SKA-Bench instances, which includes a question, an answer, positive knowledge units, and noisy knowledge units. To evaluate the SK understanding capabilities of LLMs in a fine-grained manner, we expand the instances into four fundamental ability testbeds: Noise Robustness, Order Insensitivity, Information Integration, and Negative Rejection. Empirical evaluations on 8 representative LLMs, including the advanced DeepSeek-R1, indicate that existing LLMs still face significant challenges in understanding structured knowledge, and their performance is influenced by factors such as the amount of noise, the order of knowledge units, and hallucination phenomenon. Our dataset and code are available at https://github.com/zjukg/SKA-Bench.

## introduction with the rapid development of large language models (llms) @xcite , structured knowledge (sk), such as knowledge graphs (kg) @xcite and tables, still remain essential due to their systematic and rigorous organizational formats. on the one hand, structured knowledge is usually present in various * corresponding author. ## conclusion in this paper, we introduce a fine-grained structured knowledge (sk) understanding benchmark, ska-bench, designed to provide a more comprehensive and rigorous evaluation for llms in understanding sk. the instances in ska-bench consist of a question, an answer, positive knowledge units, and noisy knowledge units, offering greater flexibility and scalability. through varying the order and scale of knowledge units within the knowledge base, we construct four specialized testbeds to evaluate key capabilities: noise robustness, order insensitivity, information integration, and negative rejection. empirical results demonstrate that even powerful llms like gpt-4o and deepseek-r1 still lack comprehensive understanding and reasoning capabilities for sk. their performance is significantly influenced by factors such as the amount of noise, order of knowledge units, and hallucinations.