Enhancing Unsupervised Sentence Embedding through Knowledge-Based Domain-Oriented Data Augmentation

Kernekoncepter

A novel pipeline-based data augmentation method that leverages large language models to synthesize domain-specific datasets, enhancing fine-grained sentence representation learning.

Resumé

The paper introduces a novel pipeline-based data augmentation method that utilizes large language models (LLMs) to enhance unsupervised sentence embedding models. The key highlights are:

Knowledge Extraction and Integration:

Extracts entities and quantities from the source data samples and constructs an entity knowledge graph.
Leverages the entity knowledge graph to generate more diverse positive and negative samples using LLMs.

Data Synthesis via LLMs:

Employs three types of prompts to generate positive and negative samples: rewriting prompt, syntactic antisense prompt, and entity revision prompt.
The entity revision prompt utilizes the entity knowledge graph to replace entities in a way that maintains semantic relevance.

Gaussian-decayed Gradient-assisted Contrastive Sentence Embedding (GCSE):

Trains the GCSE model in two stages:

Stage 1: Trains an evaluation model on the combination of domain and general data.
Stage 2: Trains the GCSE model on the filtered synthesized data, using the evaluation model to mitigate the impact of noise in the generated data.

Introduces a Gaussian-decayed function to align the distances of hard negative samples between the GCSE encoder and the frozen evaluation encoder, reducing the influence of false negative samples.

The experimental results demonstrate that the proposed approach achieves state-of-the-art performance on semantic textual similarity (STS) tasks while utilizing fewer synthetic data samples and lesser LLM parameters, showcasing its efficiency and robustness.

Statistik

The ratio of sample numbers between domain data and general data was 1:3.
The filtering thresholds of α and β are set as 0.9 and 0.75, respectively.
The temperature of τ is set as 0.05, and the σ of the Gaussian-decayed function is set as 0.01.

Citater

"To enhance the model's capacity for distinguishing these fine-grained distinctions, it is necessary to implement meticulous data augmentation techniques specifically targeting entities and quantities."
"To mitigate the impact of noise in the generated data, we propose a Gaussian-decayed gradient-assisted Contrastive Sentence Embedding (GCSE) model."
"Experimental results demonstrate the efficiency of our model, and our method achieves state-of-the-art results on semantic textual similarity (STS) tasks."

Vigtigste indsigter udtrukket fra

Knowledge-Based Domain-Oriented Data Augmentation for Enhancing Unsupervised Sentence Embedding

by Peichao Lai,... kl. arxiv.org 09-20-2024

https://arxiv.org/pdf/2409.12887.pdf

Dybere Forespørgsler

How can the proposed data augmentation and training approach be extended to other language tasks beyond sentence embedding, such as text classification or generation?

The proposed data augmentation and training approach, which utilizes entity- and quantity-aware data synthesis through large language models (LLMs), can be effectively extended to various language tasks such as text classification and text generation.

Text Classification: The entity-aware augmentation can enhance the training dataset by generating labeled examples that reflect the nuances of different classes. For instance, by synthesizing sentences that include specific entities relevant to each class, the model can learn to recognize and classify text based on contextual cues. The framework can be adapted to create positive samples that align with the target class and negative samples that are similar but belong to different classes. This would improve the model's ability to distinguish between closely related categories, thereby enhancing classification accuracy.

Text Generation: In text generation tasks, the entity-aware approach can be utilized to generate contextually rich and semantically accurate outputs. By leveraging the knowledge graph to inform the generation process, the model can produce text that maintains coherence and relevance to the specified entities and quantities. For example, in generating narratives or reports, the model can ensure that the generated content includes accurate representations of entities and their relationships, leading to more informative and contextually appropriate outputs.

Transfer Learning: The framework can also facilitate transfer learning across different tasks by utilizing the same entity-aware data synthesis techniques to create task-specific datasets. By fine-tuning models on these augmented datasets, the performance on downstream tasks can be significantly improved, as the models would have been exposed to a broader range of examples that capture the complexities of language use in various contexts.

What are the potential limitations or drawbacks of the entity-aware data synthesis approach, and how could it be further improved to handle more complex semantic relationships?

While the entity-aware data synthesis approach presents several advantages, it also has potential limitations that need to be addressed:

Complex Semantic Relationships: The current framework primarily focuses on entities and quantities, which may not capture more intricate semantic relationships such as those involving actions, emotions, or abstract concepts. To improve this, the framework could incorporate additional layers of semantic understanding, such as relationship extraction techniques that identify and represent the interactions between entities. This could involve using more sophisticated knowledge graphs that include not just entities but also their relationships and attributes.

Noise in Synthetic Data: The generation of synthetic data can introduce noise, particularly if the LLM misinterprets the context or generates irrelevant information. To mitigate this, a more robust filtering mechanism could be implemented, possibly involving multi-stage validation where generated samples are assessed by multiple models or criteria before being included in the training set.

Scalability and Generalization: The approach may struggle with scalability when applied to larger datasets or more diverse domains. To enhance generalization, the framework could be adapted to include unsupervised learning techniques that allow the model to learn from unlabeled data across multiple domains. This could involve clustering techniques to identify and synthesize data from similar contexts, thereby enriching the training dataset without requiring extensive labeled examples.

Given the importance of domain-specific data, how could the framework be adapted to effectively leverage unlabeled data from multiple domains to enhance the generalization of the sentence embedding model?

To effectively leverage unlabeled data from multiple domains and enhance the generalization of the sentence embedding model, the framework can be adapted in several ways:

Domain Adaptation Techniques: Implementing domain adaptation strategies can help the model learn from unlabeled data across different domains. Techniques such as adversarial training can be employed to minimize the domain shift between the source (training) and target (unlabeled) domains, allowing the model to generalize better across various contexts.

Multi-Domain Knowledge Graphs: The framework can utilize multi-domain knowledge graphs that integrate information from various fields. By synthesizing data that reflects the relationships and entities across these domains, the model can learn to recognize and embed sentences that are relevant in multiple contexts, thus improving its versatility.

Self-Supervised Learning: Incorporating self-supervised learning methods can enable the model to extract meaningful representations from unlabeled data. For instance, contrastive learning can be applied to create positive and negative pairs from the unlabeled dataset, allowing the model to learn discriminative features without requiring explicit labels.

Dynamic Data Augmentation: The framework can implement dynamic data augmentation strategies that adapt based on the characteristics of the incoming unlabeled data. By continuously updating the knowledge graph and the data synthesis prompts based on new data, the model can remain relevant and effective in capturing the evolving semantics of language across different domains.

By integrating these strategies, the framework can enhance its ability to generalize from domain-specific data, ultimately leading to improved performance in sentence embedding and other related tasks.