Causality can systematically address the monsters under the bench(marks)

2025-02-07

AI Summary

Machine learning evaluation is frequently compromised by benchmark bias, data leakage, and undetected failure modes—undermining result reliability, reproducibility, and inferential validity. To address these issues, we propose a novel evaluation paradigm grounded in causal modeling: explicitly formalizing evaluative assumptions, constructing testable causal hypotheses, and guiding the design of robust benchmarks. Innovatively, we introduce Causal Abstraction Topologies (CATs)—a class of canonical structures from causal graphs—into large language model (LLM) reasoning assessment for the first time, enabling unified diagnosis of the “evaluation monster” problem. Through multiple empirical case studies, we demonstrate that our framework precisely delineates method applicability boundaries, clarifies ambiguous evaluation conclusions, and enables new evaluation pathways that are reproducible, interpretable, and extensible.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever more complex, high-level tasks make systematic evaluation more challenging. Benchmarks are plagued by various biases, artifacts, or leakage, while models may behave unreliably due to poorly explored failure modes. Haphazard treatments and inconsistent formulations of such"monsters"can contribute to a duplication of efforts, a lack of trust in results, and unsupported inferences. In this position paper, we argue causality offers an ideal framework to systematically address these challenges. By making causal assumptions in an approach explicit, we can faithfully model phenomena, formulate testable hypotheses with explanatory power, and leverage principled tools for analysis. To make causal model design more accessible, we identify several useful Common Abstract Topologies (CATs) in causal graphs which help gain insight into the reasoning abilities in large language models. Through a series of case studies, we demonstrate how the precise yet pragmatic language of causality clarifies the strengths and limitations of a method and inspires new approaches for systematic progress.

Problem

• Address biases in empirical machine learning
• Systematically evaluate complex high-level tasks
• Use causality to clarify method strengths and limitations

Innovation

• Causal framework addresses evaluation biases
• Common Abstract Topologies enhance model insights
• Causal assumptions enable precise hypothesis testing