Efficient and Scalable Supervised Clustering of Text-based Entities using Large Language Models

מושגי ליבה

A novel approach for supervised clustering of text-based entity subsets that leverages open-source large language models, captures contextual information efficiently, and introduces an augmented triplet loss function to address the challenges of directly applying triplet loss to this problem.

תקציר

The paper proposes a method called CACTUS (Context-Aware ClusTering with aUgmented triplet losS) for supervised clustering of text-based entity subsets. The key highlights are:

Context-awareness: CACTUS captures the context provided by the entity subset using a scalable inter-entity attention mechanism in the Transformer encoder, which computes a single representative embedding per entity to model inter-entity interactions efficiently.

Augmented triplet loss: The authors identify limitations in directly applying the triplet loss to supervised clustering and propose an augmented triplet loss function that introduces a neutral entity to address the issue of non-overlapping margin locations across different triplets.

Self-supervised pretraining: To further improve performance, especially with limited ground truth clusterings, the authors introduce a self-supervised clustering task inspired by text data augmentation techniques.

The proposed method is evaluated on several e-commerce query and product clustering datasets, where it significantly outperforms existing unsupervised and supervised baselines across various external clustering evaluation metrics. Ablation studies demonstrate the effectiveness of the individual components of CACTUS.

סטטיסטיקה

The average size of entity sets ranges from 5 to 46 entities across the datasets.
The average number of clusters per entity set ranges from 2.6 to 6.
The average number of entities per cluster ranges from 1.9 to 8.
The average number of words per entity ranges from 6.9 to 13.9.

ציטוטים

"We observed that powerful closed-source LLMs (such as GPT-4 (Achiam et al., 2023) and Claude (Anthropic, 2023)), known for their instruction-following abilities, can provide high-quality clusterings through prompting. However, these models become unaffordable when clustering a large number of sets, due to their high costs."
"To overcome this limitation, we aim to develop a scalable model based on an open-source LLM that can efficiently and effectively perform the clustering task."

תובנות מפתח מזוקקות מ:

Context-Aware Clustering using Large Language Models

by Sindhu Tipir... ב- arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00988.pdf

שאלות מעמיקות

How can the proposed context-aware entity embeddings be leveraged for other downstream NLP tasks beyond clustering, such as entity linking or knowledge base construction?

The context-aware entity embeddings proposed in the study can be highly beneficial for various downstream NLP tasks beyond clustering. For entity linking, these embeddings can capture the nuanced relationships between entities within a given context, enabling more accurate linking of entities across different documents or datasets. By incorporating contextual information, the model can better understand the semantics and connections between entities, leading to improved entity disambiguation and linking accuracy.
In the context of knowledge base construction, the context-aware embeddings can help in identifying and organizing entities based on their contextual relevance. By leveraging the rich contextual information encoded in the embeddings, the model can enhance the construction of knowledge graphs by linking related entities and extracting meaningful relationships between them. This can lead to more comprehensive and accurate knowledge bases that reflect the underlying semantics and connections present in the data.
Overall, the context-aware entity embeddings can serve as a powerful tool for enhancing various NLP tasks by providing a deeper understanding of the relationships and semantics between entities within a given context, thereby improving the performance and accuracy of tasks such as entity linking and knowledge base construction.

How can the proposed context-aware entity embeddings be leveraged for other downstream NLP tasks beyond clustering, such as entity linking or knowledge base construction?

The augmented triplet loss function introduced in the study addresses challenges related to non-overlapping margin locations across different triplets in the context of supervised clustering. However, there are potential limitations to consider when applying this loss function in more complex clustering scenarios.
One limitation of the augmented triplet loss function is its sensitivity to the choice of hyperparameters, such as the margin value. In scenarios where the margin is not appropriately set, the loss function may struggle to effectively separate intra-cluster and inter-cluster similarities, leading to suboptimal clustering results. Additionally, the augmented triplet loss function may face challenges in handling highly imbalanced datasets where the number of positive and negative examples varies significantly, potentially affecting the model's ability to learn meaningful cluster boundaries.
To further improve the augmented triplet loss function for more complex clustering scenarios, one approach could involve incorporating adaptive margin strategies that dynamically adjust the margin based on the data distribution. This adaptive approach can help the model adapt to varying levels of intra-cluster and inter-cluster similarities, enhancing its robustness in diverse clustering scenarios. Additionally, exploring ensemble techniques or incorporating regularization methods to prevent overfitting and improve generalization could further enhance the performance of the augmented triplet loss function in complex clustering tasks.

How can the proposed context-aware entity embeddings be leveraged for other downstream NLP tasks beyond clustering, such as entity linking or knowledge base construction?

The self-supervised clustering task introduced in the study can be extended to other domains beyond text, such as structured data or multimodal data, to enhance the generalization capabilities of the model. By leveraging self-supervised learning techniques in diverse data domains, the model can learn meaningful representations and relationships without the need for explicit supervision, leading to improved performance and adaptability in various tasks.
In the context of structured data, the self-supervised clustering task can be applied to tabular data, where the model learns to cluster rows or columns based on inherent patterns and relationships within the data. By generating diverse transformations of the data and clustering them based on these variations, the model can capture underlying structures and dependencies, facilitating tasks such as data categorization, anomaly detection, and data integration.
For multimodal data, the self-supervised clustering task can be extended to scenarios involving multiple modalities, such as images and text. By generating augmented samples that combine different modalities and clustering them based on these joint representations, the model can learn to identify cross-modal relationships and similarities, enabling tasks like cross-modal retrieval, image-text alignment, and multimodal fusion.
Overall, extending the self-supervised clustering task to other domains beyond text can enhance the model's ability to generalize across diverse data types and improve its performance in a wide range of NLP tasks, structured data analysis, and multimodal applications.