Pairwise Sentence Scoring Systems - Part 2
A deep dive into advancements that shaped this task forever.
SO MANY real-world NLP systems rely on pairwise sentence similarity scoring.
Last week, I started a two-part series that walks you through the background, the challenges with traditional approaches, optimal approaches, and implementations that help you build robust systems that rely on pairwise scoring.
The second part is available here: AugSBERT: Bi-encoders + Cross-encoders for Sentence Pair Similarity Scoring – Part 2.
Why care?
SO MANY real-world NLP systems implicitly or explicitly depend on context similarities:
A RAG system heavily relies on pairwise sentence scoring (this could be at varying levels of granularity based on how you chunk the data) to retrieve relevant context, which is then fed to the LLM for generation:
That is why RAG is considered 75% retrieval and 25% generation.
In other words, most of it boils down to how well you retrieve the relevant context.
Several question-answering systems implicitly evaluate the similarity between questions and potential answers.
Several information retrieval (IR) systems depend on scoring query-document pairs to rank the most suitable documents for a given query.
Duplicate detection engines assess whether two sentences or questions convey the same meaning. This is especially observed in community-driven platforms (Stackoverflow, Medium, Quora, etc.). For instance, Quora shows you questions related to the question you are reading answers for.
This list of tasks that depend on pairwise sentence scoring can go on and on.
But the point I am trying to make here is that pairwise sentence (paragraphs, documents, etc.) scoring is a fundamental building block in several NLP applications.
If you intend to build such systems, you need those skills and understand SOTA approaches.
And this 2-part series will help you cultivate it.
We go through the entire background in a beginner-friendly way, the challenges with traditional approaches, optimal approaches, and implementations.
Read part 1 here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – Part 1.
Read part 2 here: AugSBERT: Bi-encoders + Cross-encoders for Sentence Pair Similarity Scoring – Part 2.
Of course, if you have never heard about such systems, don’t worry since that is what we intend to cover today with proper context like we always do.
P.S. For those wanting to develop “Industry ML” expertise:
At the end of the day, all businesses care about impact. That’s it!
Can you reduce costs?
Drive revenue?
Can you scale ML models?
Predict trends before they happen?
We have discussed several other topics (with implementations) in the past that align with such topics.
Here are some of them:
Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1
Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware
Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.
Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1
Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.
Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)
Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.
Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.
All these resources will help you cultivate key skills that businesses and companies care about the most.
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