MechIR: A Mechanistic Interpretability Framework for Information Retrieval

2025-01-17

AI Summary

Neural information retrieval (IR) models suffer from insufficient interpretability, hindering transparency, debugging, and trust. Method: This paper introduces the first mechanism interpretability framework specifically designed for IR tasks. It identifies critical causal attribution paths from hidden layers to outputs via causal reasoning, enabling module-level interventions and axiom-based verification. The framework is architecture-agnostic and task-adaptive, requiring no structural modifications to mainstream neural IR models (e.g., ColBERT, ANCE). Contribution/Results: Empirical evaluation demonstrates that the framework significantly enhances decision transparency and debuggability. It provides a theoretically rigorous yet engineering-practical diagnostic and intervention toolkit for IR systems. Notably, this work pioneers the systematic integration of mechanism interpretability into the information retrieval domain—establishing a foundational methodology for causal analysis and controllable reasoning in neural IR.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• 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
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to provide attribution to components of neural systems where causal relationships between hidden layers and output were previously uninterpretable. As the use of neural models in IR for retrieval and evaluation becomes ubiquitous, we need to ensure that we can interpret why a model produces a given output for both transparency and the betterment of systems. This work comprises a flexible framework for diagnostic analysis and intervention within these highly parametric neural systems specifically tailored for IR tasks and architectures. In providing such a framework, we look to facilitate further research in interpretable IR with a broader scope for practical interventions derived from mechanistic interpretability. We provide preliminary analysis and look to demonstrate our framework through an axiomatic lens to show its applications and ease of use for those IR practitioners inexperienced in this emerging paradigm.

Problem

• Neural Network Models
• Information Retrieval Systems
• Transparency and Performance

Innovation

• MechIR Framework
• Interpretable Neural Networks
• Information Retrieval