Sentiment deviation refers to the difference between the expected sentiment of a model’s response and […]
Sentence Embeddings vs. LLM Self-Similarity: Battle of the Hallucination Detectors
Today, we’re diving into the world of hallucination detection in Large Language Models (LLMs). We’ll […]
BLEU vs BERT: Choosing the Right Metric for Evaluating LLM Prompt Responses
In the ever-evolving landscape of Natural Language Processing (NLP), evaluating the performance of Large Language […]
LLM Self-Similarity: Measuring LLM Response vs Response vs Response for Same Prompt
Explanation LLM self-similarity measures the consistency of the model’s responses to identical prompts over multiple […]
Sentence Embeddings: Measuring LLM Response vs Response vs Response for Same Prompt
Explanation Sentence embeddings represent sentences as dense vectors in a high-dimensional space. The similarity between […]
BERT Score: Measuring LLM Prompt Response Relevance
Explanation: BERT (Bidirectional Encoder Representations from Transformers) score evaluates the semantic similarity between the generated […]
BLEU Score : Measuring LLM Prompt Response Relevance
Explanation: BLEU (Bilingual Evaluation Understudy) score measures how closely a machine-generated text matches one or […]
Understanding and Measuring Hallucinations in Large Language Models
Introduction Large Language Models (LLMs) have made significant strides in natural language processing, but they […]
Determining the Appropriate Amount of Test Data for Evaluating Machine Learning Models: A Comprehensive Guide
Introduction Ensuring the quality and reliability of machine learning models necessitates adhering to industry best […]
Dealing with False Positives in Entity Recognition
What are False Positives? False positives occur when the model incorrectly identifies a piece of […]