Causal Abstractions of Neural Networks (2106.02997v2)

Published 6 Jun 2021 in cs.AI and cs.LG

Abstract: Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal theory of causal abstraction that provides rich characterizations of model-internal representations and their roles in input/output behavior. In this method, neural representations are aligned with variables in interpretable causal models, and then interchange interventions are used to experimentally verify that the neural representations have the causal properties of their aligned variables. We apply this method in a case study to analyze neural models trained on Multiply Quantified Natural Language Inference (MQNLI) corpus, a highly complex NLI dataset that was constructed with a tree-structured natural logic causal model. We discover that a BERT-based model with state-of-the-art performance successfully realizes parts of the natural logic model's causal structure, whereas a simpler baseline model fails to show any such structure, demonstrating that BERT representations encode the compositional structure of MQNLI.

Authors (4)

Atticus Geiger (35 papers)
Hanson Lu (3 papers)
Thomas Icard (22 papers)
Christopher Potts (113 papers)

Citations (177)

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Summary

Causal Abstraction in Neural NLP Models: A Formal Analysis

Introduction

In recent advancements in NLP, understanding how neural networks make decisions has gained significant traction. The paper under discussion presents a systematic analysis proving that certain neural models like BERT and LSTM operate as causal abstractions. This involves demonstrating a formal link between high-level and low-level model behaviors using causal modeling concepts.

Breaking Down the Formal Definitions

The paper dives into the nitty-gritty of defining the models involved. Here's a quick breakdown of the setups:

Base Model $C_{NatLog}$ : This is a symbolic model where Q, Adj, N, Neg, Adv, and V are variables representing different grammatical components (like quantifiers, adjectives, nouns, negatives, adverbs, and verbs).
Complex Model $:</strong> This can be either BERT or LSTM. Despite their structural differences, both can be evaluated under the same framework concerning causal relationships. These models represent sentences as grids of neural activations.</li> <li><strong>Variable Sets:</strong> <ul> <li>$ \mathcal{V}_{NatLog} $encapsulates grammatical components.</li> <li>$ \mathcal{V}_{} $includes neural representations from the first to the last layer and the final output in BERT or LSTM models.</li> </ul></li> </ol> <h3 class='paper-heading'>The Core Proof</h3><h4 class='paper-heading'>Strong Numerical Results</h4> <p>The paper claims a set of interventions prove that their crunching matches align symbolically and neurally. At a high level:</p> <ol> <li><strong>Single Token Abstraction</strong>: Each neural representation in BERT or LSTM layers influences subsequent layers, following a causal pathway much like a symbolic grammar rule.</li> <li><strong>Multiple Token Abstraction</strong>: The neural activations of multiple tokens coalesce to produce high-level grammatical constructs analogous to$ C_{NatLog}$ components.

The Bold Claim

Essentially, the paper argues that even though BERT and LSTM use vastly different mechanisms internally, under the hood, they perform operations that support symbolic causal abstraction. This provides a theoretical basis by establishing that complex neural models can be abstracted into interpretable logical operations.

Practical Implications

This analysis isn't just academic—it has meaningful real-world applications:

Model Interpretability: By understanding neural activations as causal networks, we make strides towards demystifying these models. This can be crucial for debugging and improving model explanations.

Model Simplification: If neural models can be approximated using causal abstractions, then future research might develop more efficient versions performing equivalently but requiring less computational power.

Future Prospects

Given this formal grounding, a few exciting pathways open up for future developments:

Unified Frameworks: Researchers might work on combining symbolic and neural approaches more seamlessly, benefiting from the strengths of both paradigms.

Enhanced Debugging Tools: Developers could leverage these insights to build more sophisticated debugging tools that visualize and manipulate the causal pathways within neural networks.

Robust Model Development: With a solid theoretical framework, building models that resist adversarial attacks and perform consistently across varied datasets becomes feasible.

Conclusion

All in all, the paper provides a robust theoretical underpinning showing that in terms of causal abstraction, BERT and LSTM models perform high-level grammatical reasoning. This bridges the gap between symbolic approaches in NLP and deep learning methods, promising a future where both paradigms work synergistically.

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