Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models

2024-05-03

Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

AI Summary

Neural ranking models suffer from opaque relevance judgment mechanisms, hindering interpretability and trust. Method: This paper proposes an axiomatic causal intervention framework—the first to integrate causal intervention with mechanistic interpretability—to systematically dissect how models leverage information retrieval (IR) axioms (e.g., term-frequency monotonicity) in decision-making. By localizing and validating early-layer attention heads that detect repeated tokens and characterizing their synergistic role in relevance computation, the approach achieves mechanism-level, fine-grained attribution. Contribution/Results: Experiments identify attention substructures satisfying core IR axioms—including TF monotonicity—revealing a human-verifiable, interpretable computational pathway from local token matching to global relevance aggregation. The work establishes a testable, causally grounded explanation framework for neural retrieval models, substantially enhancing their transparency, accountability, and reliability.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Learning Preferences or Rankings
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still largely unknown. Existing approaches for analyzing neural ranker behavior with respect to IR properties rely either on assessing overall model behavior or employing probing methods that may offer an incomplete understanding of causal mechanisms. To provide a more granular understanding of internal model decision-making processes, we propose the use of causal interventions to reverse engineer neural rankers, and demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms within a ranking model. We identify a group of attention heads that detect duplicate tokens in earlier layers of the model, then communicate with downstream heads to compute overall document relevance. More generally, we propose that this style of mechanistic analysis opens up avenues for reverse engineering the processes neural retrieval models use to compute relevance. This work aims to initiate granular interpretability efforts that will not only benefit retrieval model development and training, but ultimately ensure safer deployment of these models.

Problem

• Neural Model Interpretability
• Ranking Task
• Information Relevance

Innovation

• Causal Intervention
• Neural Model Interpretability
• Relevance Judgment