Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning (2210.07138v1)

Abstract: Multi-hop QA requires reasoning over multiple supporting facts to answer the question. However, the existing QA models always rely on shortcuts, e.g., providing the true answer by only one fact, rather than multi-hop reasoning, which is referred as $\textit{disconnected reasoning}$ problem. To alleviate this issue, we propose a novel counterfactual multihop QA, a causal-effect approach that enables to reduce the disconnected reasoning. It builds upon explicitly modeling of causality: 1) the direct causal effects of disconnected reasoning and 2) the causal effect of true multi-hop reasoning from the total causal effect. With the causal graph, a counterfactual inference is proposed to disentangle the disconnected reasoning from the total causal effect, which provides us a new perspective and technology to learn a QA model that exploits the true multi-hop reasoning instead of shortcuts. Extensive experiments have conducted on the benchmark HotpotQA dataset, which demonstrate that the proposed method can achieve notable improvement on reducing disconnected reasoning. For example, our method achieves 5.8% higher points of its Supp$_s$ score on HotpotQA through true multihop reasoning. The code is available at supplementary material.

• The paper introduces a counterfactual multihop QA framework that uses causal inference to reduce disconnected reasoning in multi-step question answering.
• It employs counterfactual interventions via causal graphs to disentangle direct and indirect effects, achieving a 5.8 percentage point improvement on HotpotQA.
• The approach is model-agnostic, enhancing the interpretability, robustness, and reliability of multi-hop QA systems.

Counterfactual Reasoning to Tackle Disconnected Reasoning in Multi-Hop Question Answering

Introduction

Multi-hop question answering (QA) involves synthesizing information across multiple documents or paragraphs to answer complex questions. Despite significant progress in the field, prevailing models often succumb to disconnected reasoning, exploiting shortcuts by relying on single facts rather than genuine multi-hop inference. The paper introduces a novel counterfactual multihop QA framework that leverages causal inference to mitigate disconnected reasoning. This approach achieves a meaningful improvement on the HotpotQA dataset, demonstrating the utility of counterfactual reasoning in enhancing the depth of understanding in multi-hop QA models.

Disconnected Reasoning and Counterfactual Interventions

Disconnected reasoning poses a fundamental challenge in multi-hop QA, where models find shortcuts, bypassing the intended multi-step inference process. The authors propose counterfactual interventions, employing causal graphs to model the direct and indirect effects contributing to disconnected reasoning. By disentangling these effects, the method seeks to encourage models to engage in true multi-hop reasoning rather than shortcut exploitation. This is done by manipulating the training data to include counterfactual examples, which are constructed by modifying context paragraphs or questions to block shortcut pathways.

Evaluating Reduced Disconnected Reasoning

The methodology was extensively evaluated using the HotpotQA dataset, a benchmark for multi-hop QA. Experiments demonstrated that the proposed counterfactual approach significantly mitigates disconnected reasoning, as evidenced by improved performance metrics. Notably, the Supp$_s$ score, which measures the model's reliance on supporting facts for reasoning, showed a substantial increase of 5.8 percentage points, indicating a deeper engagement in multi-hop inference tasks.

Practical and Theoretical Implications

The counterfactual multihop QA framework not only reduces disconnected reasoning but also maintains competitive accuracy on standard benchmarks. This balance underscores the potential of counterfactual reasoning as a powerful tool for enhancing the interpretability, reliability, and effectiveness of multi-hop QA models. The methodology's model-agnostic nature further broadens its applicability, promising advancements across various architectures and tasks within the field.

Future Prospects in AI Research

The paper's findings open numerous avenues for future research, particularly concerning the application of causal inference in AI. Exploring counterfactual reasoning in other domains of natural language processing and beyond could uncover new strategies for addressing challenges akin to disconnected reasoning in multi-hop QA. Moreover, refining the construction of counterfactual examples and investigating their role in training more robust models offer substantial promise for advancing AI capabilities in understanding complex, multi-faceted questions.

Conclusion

This paper presents a significant step forward in tackling the pervasive issue of disconnected reasoning in multi-hop question answering. By integrating counterfactual reasoning within a causal inference framework, the proposed method not only improves performance on the multi-hop QA task but also lays the groundwork for further exploration of causality in AI, paving the way for more sophisticated and reliable inference models.