MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models

摘要

Existing causal datasets primarily focus on the commonsense domain, where the questions mainly involve simple, single-hop direct causal relationships. When models possess the corresponding knowledge, even if they cannot understand the causal relationships, they can directly arrive at the correct answers through knowledge matching. However, LLMs often perform poorly when answering questions with complex causal structures and domain-specific expertise. To address the above challenges, we propose MDC-Bench, a multidisciplinary causal evaluation benchmark. MDC-Bench adopts a three-level causal framework consisting of 4 core causal tasks, while its sample content covers 7 representative disciplines and diverse causal structures. In view of the limited coverage of multidisciplinary knowledge during the pre-training phase, the model cannot answer questions relying on knowledge matching. The diverse causal structures force the models to grasp the internal causal logic. We also increase the task complexity through methods such as compound causal operations, aiming to enhance the discriminability among models. MDC-Bench achieves the improvement in terms of domain specialization, structural diversity, and task complexity. Through extensive evaluation, we observe that even the advanced models have substantial room for improvement. MDC-Bench not only establishes a standardized baseline for causal research but also provides valuable insights for the applying LLMs in multiple domains.