Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2502.19907v1)

Abstract: Logical reasoning is essential for LLMs to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to generalize across logically equivalent transformations. LLMs often rely on fixed sequential patterns rather than true logical understanding. To address this issue, we introduce an order-centric data augmentation framework based on commutativity in logical reasoning. We first randomly shuffle independent premises to introduce condition order augmentation. For reasoning steps, we construct a directed acyclic graph (DAG) to model dependencies between steps, which allows us to identify valid reorderings of steps while preserving logical correctness. By leveraging order-centric augmentations, models can develop a more flexible and generalized reasoning process. Finally, we conduct extensive experiments across multiple logical reasoning benchmarks, demonstrating that our method significantly enhances LLMs' reasoning performance and adaptability to diverse logical structures. We release our codes and augmented data in https://anonymous.4open.science/r/Order-Centric-Data-Augmentation-822C/.

Overview of "Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation"

The paper, titled "Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation," addresses a critical challenge in the domain of LLMs: their sensitivity to the order of logical reasoning steps. The authors introduce an innovative data augmentation framework that leverages the commutativity properties inherent in logical reasoning to enhance the adaptability and accuracy of LLMs.

Core Contributions

The authors identify a significant limitation in current LLMs — their dependence on fixed sequential patterns, which leads to decreased performance when there are variations in reasoning order. This issue affects their ability to generalize across logically equivalent transformations, often resulting in a dramatic reduction in performance. As reported, perturbations in the order of logical premises can decrease accuracy by as much as 40%, a drop from 96.7% to 0.1% under specific test conditions.

To address this, the paper presents an order-centric data augmentation method. This method is two-pronged:

1. Condition Order Augmentation: Independent premises are shuffled randomly. This process introduces variability in condition order, facilitating the LLMs' learning of logical equivalence despite variations in sequence.
2. Answer Order Augmentation: The authors employ directed acyclic graphs (DAGs) to model and reorder reasoning step dependencies. By doing so, they preserve logical correctness while allowing flexibility in reasoning path configurations.

Implementation and Experimentation

The efficacy of this approach is validated through extensive experiments on several logical reasoning benchmarks, including datasets like FOLIO, RuleTaker, and LogicNLI. The authors employ models such as LLaMA3-8B-Instruct, LLaMA2-13B-Chat, and Mistral-7B-Instruct-v0.3, across five experimental conditions to fine-tune and compare results. The strategic goal of these experiments is to evaluate the performance enhancements when models are subjected to order-centric augmentations compared to conventional fine-tuning methods.

Key Findings

The results reveal that integrating order-centric augmentations yields notable improvements in logical reasoning tasks:

• There is a consistent performance increase in models trained with condition shuffled data compared to models trained on fixed order data, with gains of up to 15% observed.
• Answer order shuffling combined with a Chain-of-Thought approach further bolsters reasoning accuracy, demonstrating increases in model performance of approximately 2% to 3% over baseline methods.

Beyond overall performance gains, the paper highlights that such augmentative practices enhance training efficiency. By controlling the degree of data augmentation to avoid overfitting, significant improvements in logical comprehension are achieved. However, the authors caution against combining condition and step order augmentations indiscriminately, as this can introduce excessive complexity and noise, potentially degrading performance.

Implications and Future Directions

The implications of this research are twofold:

• Practical Implications: Order-centric data augmentation methodologies can be adopted in training regimes to elevate the logical reasoning capabilities of contemporary LLMs. This approach shows promise in applications where logical consistency and versatility in reasoning are critical.
• Theoretical Implications: This paper emphasizes the importance of logical commutativity in enhancing model robustness and paves the way for future explorations into the integration of symbolic logical principles in LLM training.

Future work in this area might involve extending order-centric augmentation techniques beyond propositional logic to encompasse more nuanced reasoning tasks, such as those found in mathematical and scientific domains. Additionally, refining the augmentation strategies to balance complexity and model improvement remains an open question for exploration.

In conclusion, the paper provides a compelling argument and evidence for the importance of order-centric training methodologies in augmenting the logical reasoning capabilities of LLMs, presenting a noteworthy advancement in AI training techniques.