Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model

Core Concepts

A novel method for synthesizing a large-scale, high-quality knowledge graph-to-text generation dataset (WikiOFGraph) that is independent of external ontologies and leverages Large Language Models and Data-QuestEval for effective graph extraction and data curation.

Abstract

The paper introduces a novel method for synthesizing a large-scale, general-domain knowledge graph-to-text (G2T) generation dataset called WikiOFGraph. The key highlights are:

1. Limitations of existing G2T datasets:

   • Scarcity of high-quality, general-domain G2T datasets
   • Ontology-based datasets suffer from graph-text misalignment

2. Proposed method:

   • Leverages Large Language Models (LLMs) to extract graph representations directly from Wikipedia sentences
   • Utilizes Data-QuestEval, a referenceless evaluation framework, to curate well-aligned graph-text pairs
   • Generates 5.85M high-quality, ontology-free G2T samples covering a broad range of Wikipedia topics

3. Comprehensive analysis:

   • WikiOFGraph exhibits high graph-text consistency, comparable to a human-crafted dataset
   • Significantly outperforms existing ontology-based datasets in terms of domain diversity and scale
   • Fine-tuning a PLM on WikiOFGraph leads to superior performance on general-domain G2T generation tasks

4. Additional experiments and case study:

   • Demonstrate the effectiveness of Data-QuestEval in ensuring high-quality graph-text alignments
   • Identify and address common issues in LLM-generated graph-text pairs, such as incomplete sentences and ambiguous pronouns

The proposed method provides a scalable and effective solution for generating high-quality G2T data without relying on proprietary LLMs, external ontologies, or extensive human involvement, making it a valuable contribution to advancing G2T generation research.

Stats

The average number of triplets per sample in WikiOFGraph is 3.62.
WikiOFGraph contains 140,733 unique predicates and 8.2M unique entities.

Quotes

"Recent advancements in Pretrained Language Models (PLMs) have improved G2T performance, but their effectiveness depends on datasets with precise graph-text alignment."
"To address this issue, we introduce an effective method for generating high-quality G2T dataset that integrates LLM with Data-QuestEval (Rebuffel et al., 2021)."
"Experimental results demonstrate that PLM fine-tuned on WikiOFGraph outperforms those trained on other datasets across various evaluation metrics."

Key Insights Distilled From

Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model

by Daehee Kim, ... at arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07088.pdf

Deeper Inquiries

How can the proposed method be extended to support multilingual G2T generation tasks?

To extend the proposed method for multilingual Graph-to-Text (G2T) generation tasks, several strategies can be implemented. First, leveraging multilingual Large Language Models (LLMs) that are pre-trained on diverse language corpora would be essential. These models, such as mBART or mT5, can generate text in multiple languages, allowing for the synthesis of graph-text pairs across different linguistic contexts.
Second, the source sentence collection process can be adapted to include multilingual Wikipedia articles, ensuring that the dataset encompasses a wide range of languages. This would involve implementing a rule-based algorithm similar to the one used for English, but tailored to accommodate the syntactic and semantic structures of various languages.
Additionally, the graph extraction process should be refined to account for language-specific nuances, such as idiomatic expressions and cultural references. This could involve training the LLM on language-specific datasets to enhance its understanding of context and meaning in different languages.
Finally, Data-QuestEval can be adapted to evaluate the consistency of graph-text pairs in multiple languages, ensuring that the generated text accurately reflects the information contained in the graph representations. By implementing these strategies, the proposed method can effectively support multilingual G2T generation tasks, broadening its applicability and impact.

What alternative approaches could be explored to further improve the consistency between the generated graph representations and the source text?

To further improve the consistency between generated graph representations and source text, several alternative approaches can be explored. One approach is to enhance the graph extraction process by incorporating more sophisticated natural language processing techniques, such as dependency parsing and semantic role labeling. These techniques can provide deeper insights into the relationships between entities in the text, leading to more accurate graph representations.
Another approach is to implement a feedback loop where the generated text is evaluated against the graph representations iteratively. This could involve using reinforcement learning techniques, where the model is trained to optimize its output based on the alignment between the generated text and the graph. By continuously refining the output through this feedback mechanism, the model can learn to produce more consistent and accurate representations.
Additionally, employing ensemble methods that combine outputs from multiple models can enhance consistency. By aggregating predictions from different LLMs or using a mixture of experts approach, the final output can benefit from the strengths of various models, leading to improved alignment between the graph and text.
Lastly, exploring advanced prompt engineering techniques can also yield better results. By crafting prompts that explicitly guide the LLM to focus on specific aspects of the graph during text generation, the model can be steered towards producing more coherent and contextually relevant outputs.

How can the WikiOFGraph dataset be leveraged to enhance the graph-based reasoning capabilities of large language models?

The WikiOFGraph dataset can significantly enhance the graph-based reasoning capabilities of large language models (LLMs) in several ways. First, the dataset's large scale of 5.85 million graph-text pairs provides a rich source of structured knowledge that LLMs can learn from. By fine-tuning on this dataset, LLMs can develop a better understanding of how to interpret and generate text based on graph representations, thereby improving their reasoning abilities.
Second, the high graph-text consistency achieved through the Data-QuestEval filtering process ensures that the training data is reliable and accurately reflects the relationships between entities. This consistency allows LLMs to learn more effectively, as they can trust that the information in the graphs corresponds directly to the text, facilitating better reasoning and inference.
Moreover, the diverse range of topics covered in the WikiOFGraph dataset enables LLMs to generalize their reasoning capabilities across various domains. This domain diversity is crucial for developing models that can perform well in real-world applications, where they may encounter unfamiliar topics or contexts.
Additionally, the dataset can be used to create specialized tasks that challenge LLMs to perform graph-based reasoning. For instance, researchers can design evaluation benchmarks that require models to answer questions or generate explanations based on the graph representations, thereby directly assessing and enhancing their reasoning capabilities.
Finally, the WikiOFGraph dataset can serve as a foundation for further research into graph-based reasoning techniques, such as integrating symbolic reasoning with neural approaches. By combining the strengths of structured knowledge from the dataset with the flexibility of LLMs, researchers can explore innovative ways to improve reasoning processes in natural language understanding and generation tasks.