On the Paradox of Learning to Reason from Data (2205.11502v2)

Abstract: Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each other: BERT attains near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems. We also show that it is infeasible to jointly remove statistical features from data, illustrating the difficulty of learning to reason in general. Our result naturally extends to other neural models and unveils the fundamental difference between learning to reason and learning to achieve high performance on NLP benchmarks using statistical features.

• The paper demonstrates that while BERT achieves near-perfect accuracy in controlled settings, it struggles to generalize to new data distributions.
• It empirically evaluates SimpleLogic tasks to reveal that transformer models often rely on statistical biases rather than authentic logical reasoning.
• The study highlights the need for novel architectures and training methods to overcome the reliance on superficial patterns and achieve true reasoning.

An Analysis of the Difficulties in Training Neural Models for Logical Reasoning

The paper "On the Paradox of Learning to Reason from Data" investigates the challenges of training transformer-based neural models, such as BERT, to conduct logical reasoning. This is explored within a controlled problem space called SimpleLogic, which focuses on logical reasoning problems represented in natural language. The authors present an empirical contradiction: BERT achieves remarkable accuracy on in-distribution tests but struggles with generalization across different data distributions within the same domain. This paper provides critical insights into the underlying complexities of logical reasoning for neural models and highlights a fundamental distinction between learning statistical patterns and genuine reasoning.

Summary of Findings

A key observation made by the authors is that while BERT can achieve near-perfect accuracy within a specific training distribution, it fails to generalize beyond it. This failure is evident even when the reasoning task remains consistent across other data distributions. The paper constructs SimpleLogic, a domain of propositional logic that reduces linguistic variance, and employs BERT to determine if neural networks can learn reasoning capabilities from structured natural language descriptions.

Contributions

1. Demonstration of BERT's Capacity:
   • The authors theoretically and practically confirm that BERT has sufficient capacity to model the correct reasoning function for SimpleLogic via explicit parameterization. This suggests that the architecture, in theory, should support logical reasoning if properly trained.
2. Empirical Evaluation:
   • Training BERT on datasets generated via Rule-Priority (RP) and Label-Priority (LP) sampling methods showed that models achieve high accuracy in distribution but fail to generalize across distributions. This points towards overfitting to statistical features intrinsic to the training data rather than learning robust reasoning.
3. Statistical Features and Their Impact on Generalization:
   • The paper identifies statistical features inherent to logical reasoning problems, such as the number of rules, and demonstrates how models might exploit these features to improve in-distribution performance but at the cost of generalization capacity.
   • Systematically removing these statistical features improved generalization, but the computational demands of jointly removing multiple statistical features are highlighted as infeasibly high.
4. Parodox Explanation:
   • The paradox emerges from the conflict between BERT's successful in-distribution performance and its failure to generalize due to reliance on statistical features that do not align with logic-based reasoning functions.

Implications

This paper raises fundamental questions about the efficacy of current neural models in learning structured cognitive functions like logical reasoning. It suggests that successful generalization requires models to transcend pattern recognition based on statistical features and fully internalize logical reasoning paradigms. Achieving this might necessitate novel architectures or training paradigms beyond current transformer-based frameworks.

Speculation on Future Directions

Addressing the challenges outlined in this paper could redirect the course of AI research in several ways:

• New Training Techniques: Introducing training methods designed to minimize reliance on superficial statistical features, potentially involving adversarial or constraint-based learning.
• Architectural Advances: Development of architectures that inherently prioritize logic-based processing over statistical correlation.
• Better Datasets: Creation of datasets where statistical features are systematically controlled or eliminated to facilitate genuine reasoning learning.

In conclusion, the paper provides an profound exploration of the limits of current models like BERT when tasked with logical reasoning and highlights the critical gap between learning intricate reasoning and exploiting dataset-specific biases. This paper sets the stage for future research efforts aimed at overcoming these limitations to achieve more robust and logically sound AI systems.