Large Language Models Enable Few-Shot Clustering (2307.00524v1)

Abstract: Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the user's intent. Existing approaches to semi-supervised clustering require a significant amount of feedback from an expert to improve the clusters. In this paper, we ask whether a LLM can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We show that LLMs are surprisingly effective at improving clustering. We explore three stages where LLMs can be incorporated into clustering: before clustering (improving input features), during clustering (by providing constraints to the clusterer), and after clustering (using LLMs post-correction). We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality, and that LLMs enable a user to make trade-offs between cost and accuracy to produce desired clusters. We release our code and LLM prompts for the public to use.

• The paper demonstrates that LLMs can enhance semi-supervised clustering by enriching features through keyphrase expansion before clustering.
• The paper shows that LLMs serve as pseudo-oracles by providing pairwise constraints that guide the clustering process with minimal expert input.
• The paper reveals that using LLMs for post-clustering correction, despite modest gains, underscores their potential in automating error detection for improved clustering quality.

An Expert Analysis of "LLMs Enable Few-Shot Clustering"

The paper "LLMs Enable Few-Shot Clustering" by Vijay Viswanathan et al. explores the utility of LLMs to improve the efficiency and effectiveness of semi-supervised clustering with minimal expert intervention. The authors propose that LLMs can be strategically incorporated across different stages of the clustering process to democratize clustering tasks typically dependent on extensive expert annotations.

Key Contributions

The research investigates three distinct intervention points in the clustering pipeline where LLMs can enhance outcomes effectively: before clustering for feature enrichment, during clustering through constraints, and post-clustering for corrections. Each of these methodologies aims to address the fundamental issue in semi-supervised clustering: reducing the burden on domain experts by exploiting the generalization capabilities of LLMs.

1. Pre-clustering Feature Enhancement: By using LLMs to generate enriched textual representations via keyphrase expansion, the authors have demonstrated a notable improvement in cluster quality. This method allows the model to capture relevant task-specific features and enhances clustering algorithms' interpretability and effectiveness.
2. Pseudo-Oracle Constraint Application: The paper employs LLMs as oracles to provide pairwise constraints, mimicking expert feedback. This potentially reduces the need for significant human interventions by efficiently guiding clustering algorithms using pseudo-labeled constraints, striking a balance between clustering accuracy and operational costs.
3. Post-Clustering Correction: Although the added value is less pronounced here, LLMs assist in correcting erroneous cluster assignments by re-evaluating the points with low clustering confidence. Despite the limited improvements, this aspect of LLM intervention points to the nuanced understandings LLMs can bring to clustering outputs.

Empirical Validation

Extensive experimentation across several datasets, including entity canonicalization and text clustering tasks, validates the efficacy of these methodologies. Notably, using LLMs for keyphrase expansion yielded consistent cluster quality improvements and set new benchmarks on canonicalization datasets. However, the empirical results for post-correction were modest, indicating potential areas for further refinement and research.

Implications and Future Directions

This work highlights the potential of LLMs in reducing the feedback loop between humans and machine learning models, effectively lowering the cost and increasing accessibility of sophisticated clustering tasks. The implications stretch across multiple domains where textual data clustering is critical—such as information retrieval, customer intent classification, and topic modeling.

Moving forward, this research sponsors several intriguing directions:

• Scalability and Efficiency: Given the computational costs associated with LLMs, future work should explore optimizing these interventions to maintain cost-effectiveness without sacrificing performance gains.
• Integration with Smaller Models: Exploring methods to incorporate LLM insights within smaller models or through model distillation techniques could make these techniques more widely applicable, especially in resource-constrained settings.
• Enhanced Feature Engineering: Additional investigation into more sophisticated feature engineering techniques leveraging LLMs, potentially incorporating domain-specific knowledge, could yield further improvements in clustering precision.

Conclusion

The research presented in "LLMs Enable Few-Shot Clustering" illustrates the transformative potential of LLMs in semi-supervised clustering tasks. By strategically incorporating LLMs, it is possible to significantly enhance clustering processes, offering improved accuracy while reducing the dependency on expert feedback. This work sets a foundation for future exploration into leveraging LLMs for diverse clustering applications, fostering more intelligent and automated data organization systems in the field of artificial intelligence.