Investigating Pretrained LLMs for Graph-to-Text Generation
Graph-to-text generation has been a significant area of focus due to its potential to transform structured data representations, such as knowledge graphs (KGs) and abstract meaning representations (AMRs), into coherent and fluent natural language text. The paper explores the application of pretrained LLMs (PLMs), specifically BART and T5, in this domain. The paper further investigates how task-adaptive pretraining strategies can enhance the performance of these models on graph-to-text generation tasks across different domains including AMR, Wikipedia knowledge graphs, and scientific KGs.
Approach and Experimental Setup
The paper examines the efficacy of utilizing BART and T5, both rooted in Transformer-based encoder-decoder architectures, for converting graph data into natural language. These models were chosen for their strength in conditional text generation, which is pertinent to the graph-to-text task. The research involved fine-tuning the PLMs on tasks across varied graph datasets such as AMR (LDC2017T10), WebNLG, and AGENDA. A critical aspect of this research involves implementing task-specific adaptation through task-adaptive pretraining strategies: LLM adaptation (lma) and supervised task adaptation (sta). These methods aim to leverage additional task-relevant data to close the domain gap between pretraining and fine-tuning phases.
Key Findings
The outcomes of this paper highlight several pivotal points:
- Performance: The application of BART and T5 showed significant improvements, establishing new state-of-the-art results on the studied datasets. For instance, the paper reported BLEU scores of 49.72 on AMR and 59.70 on WebNLG, which reflect substantial performance enhancements over previous models.
- Task-Adaptive Pretraining: The introduction of task-adaptive pretraining, especially the supervised task adaptation, further boosts PLM performance, indicating the importance of adapting PLMs with domain-specific data.
- Impact of Graph Structure: An intriguing observation from this paper is the limited reliance of PLMs on explicit graph structures. The research found that PLMs often perform well even when the structured data input, such as KGs, are significantly simplified to a sequence of node and edge labels. This suggests that these models might be leveraging more on their LLMing capabilities, possibly recalling factual knowledge from their pretraining phase.
Human Evaluation
Complementing the quantitative evaluation, the paper incorporates human evaluation criteria, assessing the fluency, meaning similarity, and semantic adequacy of generated text. The results indicate that PLM-generated text not only achieves remarkable automatic scores but also rates highly in human evaluations, often exceeding the fluency of human references.
Implications and Future Directions
This research illustrates the effectiveness of PLMs in graph-to-text tasks and highlights their unsupervised learning capabilities. A key implication is the potential for these models to be used in applications like automatic report generation and content creation from structured data repositories. However, the findings also raise questions about the faithfulness of the generated text to the input graph structures. Future research might focus on enhancing PLM architectures to better incorporate and respect graph structures while still maintaining their linguistic capabilities. Another promising direction could be exploring more robust task-specific adaptation techniques that ensure these models generate texts deeply aligned with the intended input structures.
Overall, this paper bridges the gap between sophisticated pretrained LLMs and graph-based data generation, setting a foundation for further exploration and refinement in the domain of NLP and automated text generation from structural data sources.