Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills (2107.07261v1)

Abstract: Models pre-trained with a LLMing objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning. In this work, we propose to leverage semi-structured tables, and automatically generate at scale question-paragraph pairs, where answering the question requires reasoning over multiple facts in the paragraph. We add a pre-training step over this synthetic data, which includes examples that require 16 different reasoning skills such as number comparison, conjunction, and fact composition. To improve data efficiency, we propose sampling strategies that focus training on reasoning skills the model is currently lacking. We evaluate our approach on three reading comprehension datasets that are focused on reasoning, and show that our model, PReasM, substantially outperforms T5, a popular pre-trained encoder-decoder model. Moreover, sampling examples based on current model errors leads to faster training and higher overall performance.

• The paper proposes generating synthetic reasoning examples from semi-structured tables to pre-train language models for enhanced reasoning skills.
• It generates question-context-answer triplets from tables, introducing the PReasM model and adaptive sampling strategies which yield significant performance gains on complex reasoning datasets like IIRC and MMQA.
• The methodology provides a robust framework for future LM pre-training using structured data, with potential for expanding to cross-modal reasoning and domain-specific applications.

An Analytical Overview of "Turning Tables: Generating Examples from Semi-structured Tables for Endowing LLMs with Reasoning Skills"

The paper "Turning Tables: Generating Examples from Semi-structured Tables for Endowing LLMs with Reasoning Skills" introduces a novel methodology for enhancing the reasoning capabilities of pre-trained LLMs (LMs) by leveraging semi-structured tables to generate synthetic datasets. This approach addresses the documented limitations of LMs in tasks requiring reasoning rather than mere linguistic knowledge.

Synthesis of Approach

This research aims to improve symbolic reasoning in LMs by generating substantial quantities of synthetic data from Wikipedia's semi-structured tables. It involves creating question-context-answer triplets from these tables using templates designed for 16 specific reasoning skills, such as numerical comparison and fact composition. The method takes advantage of tables' structured nature to automate reasoning-focused data generation efficiently.

The technique introduces a new pre-training phase on the synthetic dataset using a model termed PReasM. A critical component of this approach is the implementation of varied sampling strategies, including error-driven sampling techniques that prioritize examples based on model performance discrepancies.

Empirical Validation

The effectiveness of PReasM is evaluated across three datasets—DROP, IIRC, and MMQA, all requiring complex reasoning. PReasM outperforms the baseline T5 model by substantial margins in F$_1$ metrics, demonstrating its enhanced reasoning ability. Notably, PReasM establishes new benchmarks on the IIRC and MMQA datasets, indicating significant improvements attributable to its reasoning-focused pre-training model. However, on tasks like DROP which require extensive numerical manipulation, specialist models like GenBERT leveraging specific numerical architectures still maintain superiority.

Numerical and Theoretical Contributions

The results highlight the strong numeric gains across datasets, including a 7.6 F$_1$ point increase over T5 on DROP and substantial improvements in task-specific accuracy, such as up to 40 F$_1$ points on date difference questions. These improvements underscore the model's adaptability in acquiring complex reasoning skills.

From a theoretical perspective, the authors deftly integrate sampling strategies to optimize training resource allocation and emphasize the importance of adaptive sampling in optimizing learning dynamics across heterogeneous task spaces. Momentum sampling, as proposed, demonstrates promise in dynamically balancing training across tasks with varying difficulty levels and ceiling performances, potentially mitigating overfitting on easier tasks or underfitting on more complex ones.

Future Directions

The methodology provides a robust framework for future exploration in pre-training strategies that incorporate additional data sources and domain-specific constraints. It suggests that synthetic data generation from structured sources can effectively bridge current LM deficits in reasoning, thus potentially expanding the generalization capabilities of LMs in unseen environments.

The prospect of integrating cross-modal reasoning, leveraging visual or multimodal inputs alongside text and table data, presents a fertile area for expansion following this groundwork. Additionally, embracing highly automated reasoning data-generation techniques aligned with application-specific needs, such as scientific literature or industry-specific datasets, would further enhance the practical applicability of this approach.

In conclusion, "Turning Tables" offers a rich contribution to the field's understanding of reasoning in LMs, providing empirical evidence of the benefits of tailored pre-training pathways employed in conjunction with synthetic data-driven augmentation. This work paves the way for more sophisticated and efficiently-trained reasoning-enhanced LLMs, with potential ripple effects across the broader AI landscape.