The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability

2024-08-02

arXiv.org

AI Summary

Explanability research in large language models (LLMs) suffers from the absence of a unified theoretical framework, ambiguous definitions of causal mediators, and difficulties in cross-method comparison. Method: This work systematically introduces causal mediation analysis into LLM interpretability research—establishing, for the first time, a theoretical framework grounded in causal mediation. It proposes a two-dimensional taxonomy: one dimension classifies mediators by type (local vs. global, linear vs. nonlinear, explicit vs. implicit); the other categorizes search paradigms (gradient-based, perturbation-based, decomposition-based, activation-based). Contribution/Results: The framework introduces the novel “interpretability–efficiency trade-off” evaluation dimension, advocates standardized benchmarking, and advances modeling of higher-order, nonlinear abstract mediators. It provides principled guidance for method selection, clarifies the applicability boundaries of existing techniques, and charts a roadmap for next-generation interpretability methods grounded in causal reasoning.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficult to measure progress and compare the pros and cons of different techniques. Furthermore, while mechanistic understanding is frequently discussed, the basic causal units underlying these mechanisms are often not explicitly defined. In this paper, we propose a perspective on interpretability research grounded in causal mediation analysis. Specifically, we describe the history and current state of interpretability taxonomized according to the types of causal units (mediators) employed, as well as methods used to search over mediators. We discuss the pros and cons of each mediator, providing insights as to when particular kinds of mediators and search methods are most appropriate depending on the goals of a given study. We argue that this framing yields a more cohesive narrative of the field, as well as actionable insights for future work. Specifically, we recommend a focus on discovering new mediators with better trade-offs between human-interpretability and compute-efficiency, and which can uncover more sophisticated abstractions from neural networks than the primarily linear mediators employed in current work. We also argue for more standardized evaluations that enable principled comparisons across mediator types, such that we can better understand when particular causal units are better suited to particular use cases.

Problem

• Unifying mechanistic interpretability through causal mediation analysis
• Defining causal units underlying language model mechanisms
• Evaluating mediator types and search methods for interpretability

Innovation

• Causal mediation analysis for interpretability
• Taxonomizing mediators and search methods
• Standardized evaluations for future work