InsurTech innovation using natural language processing

arXiv:2507.21112 · cs.CL, cs.LG, stat.ML · Submitted 2025-07-12 · Read on arXiv

cs.CL, cs.LG, stat.ML

Submitted: 2025-07-12

Updated: 2026-09-23

Terminology

• Accuracy — 39× in this paper · explained in 1 episode
• Embeddings — 33× in this paper · explained in 2 episodes
• Language Model — 19× in this paper
• Transformer — 14× in this paper · explained in 2 episodes
• Clustering — 11× in this paper · explained in 2 episodes
• Natural Language Processing — 11× in this paper
• RAG — 10× in this paper · explained in 3 episodes
• Predictive Modeling — 8× in this paper · explained in 2 episodes
• Core — 7× in this paper · explained in 2 episodes
• Neural Networks — 6× in this paper
• Interpretability — 5× in this paper · explained in 15 episodes
• Attention Mechanism — 4× in this paper · explained in 3 episodes
• Inverse Document Frequency — 4× in this paper
• Latent Semantic Analysis — 4× in this paper
• Recurrent Neural Network — 4× in this paper
• Term Frequency — 4× in this paper
• Vector Representations — 4× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 3× in this paper
• Latent Dirichlet Allocation — 3× in this paper
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Feature Engineering — 2× in this paper · explained in 3 episodes
• Long Short-Term Memory — 2× in this paper
• Mean Absolute Error — 2× in this paper
• North American Industry Classification System — 2× in this paper
• Objective Function — 2× in this paper · explained in 2 episodes
• Percentage Error — 2× in this paper
• Retrieval-Augmented Generation — 2× in this paper
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
• Root Mean Squared Error — 2× in this paper
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Area Under Precision-Recall Curve — 1× in this paper
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalized Linear Model — 1× in this paper
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Maximum Likelihood Estimation — 1× in this paper
• Named Entity Recognition — 1× in this paper
• Named Entity Recognition (NER) — 1× in this paper · explained in 2 episodes
• Optical Character Recognition — 1× in this paper
• Pointwise Mutual Information (PMI) — 1× in this paper · explained in 3 episodes
• Positive Pointwise Mutual Information — 1× in this paper
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Rapid Automatic Keyword Extraction — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Singular Value Decomposition — 1× in this paper
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Enriching Word Vectors with Subword Information
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• DeepSeek-V3 Technical Report
• The Llama 3 Herd of Models
• Generating Sequences With Recurrent Neural Networks
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Efficient Estimation of Word Representations in Vector Space
• Qwen2.5 Technical Report
• Sequence to Sequence Learning with Neural Networks
• Gemma: Open Models Based on Gemini Research and Technology
• Gemma 2: Improving Open Language Models at a Practical Size
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Qwen2 Technical Report

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