Generating Datasets with Pretrained Language Models (2104.07540v3)

Abstract: To obtain high-quality sentence embeddings from pretrained LLMs (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.

• The paper introduces an innovative method that utilizes pretrained language models to generate labeled datasets without extensive human annotation.
• It employs a two-step process to produce diverse and accurate text pairs using a self-debiasing strategy and effective label smoothing.
• Experimental results across STS benchmarks show the unsupervised models outperform some supervised alternatives, validating its practical significance.

Overview of "Generating Datasets with Pretrained LLMs"

The paper "Generating Datasets with Pretrained LLMs" by Timo Schick and Hinrich Schütze explores an innovative approach to generating labeled datasets for training smaller LLMs without relying on large-scale annotated data. This paper leverages the inherent capabilities of large Pretrained LLMs (PLMs) to create high-quality sentence embeddings indispensable for various NLP tasks, particularly in scenarios where obtaining human-labeled data is resource-intensive.

Methodology

The authors tackle the challenge by employing generative PLMs to automatically synthesize datasets of labeled text pairs. Their approach mimics the output typically produced by human annotators, circumventing the necessity for amendments to the pretrained models' underlying objectives or extensive finetuning on human-annotated datasets. The key novelty lies in utilizing PLM's ability to follow instructions to fabricate datasets from scratch, labeled with semantic similarity scores, which are then used to train smaller, more efficient models.

The method employs a two-step process: initially, a set of textual data is generated if not readily available. Then, using the self-debiasing approach to ensure diversity and accuracy, task-specific text pairs are crafted across varying degrees of similarity. This setup enables the creation of sentence similarity datasets like STS-Dino, entirely free from manual annotation efforts.

Experimental Evaluation

The efficacy of this approach was evaluated across several semantic textual similarity tasks including the STS Benchmark and the SICK-Relatedness dataset. The unsupervised models trained using Dino-generated datasets were shown to outperform both recent unsupervised methods and some supervised alternatives on average. Remarkably, the proposed approach achieved higher performance than supervised models on four out of six STS tasks, indicating the high quality of the synthetically generated datasets.

Detailed Insights and Implications

1. Self-Debiasing Approach: The use of a self-debiasing mechanism was significant for generating non-trivially diverse examples, ensuring that models do not merely replicate near-identical text but create meaningful and varied examples.
2. Label Smoothing and Data Augmentation: These techniques were crucial for managing the innate noise in generated datasets, suggesting practical pathways for enhancing the robustness of models trained under unsupervised settings.
3. Human Evaluation: A supplementary analysis through human evaluation provided insights into the qualitative aspect of the dataset, noting areas where PLMs showed competency in generating logical sentence pairs and also identifying common pitfalls.
4. Future Directions: The paper opens avenues for improving dataset quality further by adjusting instruction sets or incorporating more sophisticated filtering mechanisms. Additionally, future work could explore expanding this approach to different NLP domains or tasks.

Practical and Theoretical Implications

Practically, this research provides a cost-effective and efficient pathway to generate massive labeled datasets, significantly reducing the reliance on human annotation which is often a bottleneck in model development pipelines. Theoretically, it challenges the presumption that high-quality dataset generation inherently necessitates human intervention, introducing a paradigm where machines autonomously create training resources under guided instructions.

In conclusion, this paper contributes a substantial advancement in dataset generation methodology, allowing researchers to harness the capabilities of large PLMs for producing datasets that are both resource-effective and high-performing. The insights gained from this work also offer promising directions for ongoing efforts to optimize PLM fine-tuning and deployment in unsupervised or low-data environments.