Summary of Human-interpretable Clustering Of Short-text Using Large Language Models, by Justin K. Miller et al.
Human-interpretable clustering of short-text using large language models
by Justin K. Miller, Tristram J. Alexander
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of clustering short text documents, which is typically hindered by low word co-occurrence between documents. Large Language Models (LLMs) are leveraged to generate embeddings that capture semantic nuances in short text. Gaussian Mixture Modelling (GMM) is used to find clusters in the embedding space, yielding more distinctive and human-interpretable results compared to traditional methods like doc2vec and Latent Dirichlet Allocation (LDA). The clustering approach’s effectiveness is evaluated using human reviewers and a generative LLM, which shows good agreement with human assessments. This study highlights the potential for large language models to bridge the “validation gap” between cluster production and interpretation. Furthermore, the comparison between LLM-coding and human-coding reveals biases in each method, questioning the conventional reliance on human coding as the gold standard for cluster validation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding groups of similar short texts, which is a tricky task because these texts don’t have many words in common. The researchers use special language models to create a map that captures the meaning behind each text. They then use a statistical method called Gaussian Mixture Modelling (GMM) to find clusters on this map. Surprisingly, their approach produces better results than traditional methods and is more understandable by humans. To check how well it works, they had both computers and human reviewers do the clustering task and compared the results. This study shows that these special language models can help us understand groups of short texts better and might even change how we evaluate whether a clustering method is good or not. |
Keywords
» Artificial intelligence » Clustering » Doc2vec » Embedding space
