PRISM: LLM-Guided Semantic Clustering for High-Precision Topics

PRISM is a topic modeling framework that combines Large Language Models (LLMs) with semantic clustering for high-precision topic identification. It fine-tunes sentence encoders to balance semantic depth with cost-effectiveness and interpretability for actionable insights.

intermediate1 hour5 steps

The play

Prepare Text Corpus
Collect and preprocess your unstructured text data. Clean, normalize, and segment the text into meaningful units (e.g., sentences, paragraphs) suitable for encoding.
Select/Fine-Tune Sentence Encoder
Choose a pre-trained sentence encoding model (e.g., Sentence-BERT, a transformer-based model). For domain-specific precision, fine-tune this model on a relevant dataset to enhance its contextual understanding, leveraging principles of LLM guidance.
Generate Semantic Embeddings
Use the selected or fine-tuned sentence encoder to transform your preprocessed text units into high-dimensional semantic embeddings. These vectors represent the contextual meaning of each text unit.
Perform Latent Semantic Clustering
Apply a clustering algorithm to the generated embeddings. Techniques like UMAP for dimensionality reduction followed by HDBSCAN or K-Means can effectively group semantically similar embeddings into latent topics.
Interpret and Refine Topics
Analyze the clusters to derive meaningful topic labels. Evaluate the precision and coherence of the identified topics, refining parameters or re-evaluating the encoding/clustering steps as needed to achieve high-precision topic identification.

Starter code

from sentence_transformers import SentenceTransformer
import numpy as np

# Load a pre-trained sentence embedding model (e.g., a small, efficient one)
model = SentenceTransformer('all-MiniLM-L6-v2')

# Example text data for topic modeling
texts = [
    "PRISM combines LLMs and clustering for precise topic identification.",
    "This framework fine-tunes sentence encoders for better semantic representations.",
    "Achieve high-precision topics with cost-effective and interpretable methods.",
    "Machine learning models often require extensive data preprocessing.",
    "Data cleaning is a critical step in any NLP pipeline.",
    "The interpretability of topic models is crucial for practical applications."
]

# Generate embeddings for the texts
embeddings = model.encode(texts, convert_to_tensor=False)

print(f"Generated {len(embeddings)} embeddings, each with dimension {embeddings.shape[1]}.")
print("First 5 dimensions of the first embedding:\n", embeddings[0][:5])

Source

Paperarxiv.org