Improving Text Embeddings with Large Language Models: Analysis of Training Hyperparameters | HackerNoon
Briefly

from Hackernoon7 months ago
The Mistral-7B initialization shows a clear advantage over LLaMA-2 7B, reinforcing earlier findings about its performance capabilities, particularly in multilingual retrieval scenarios.
Hackernoonhttps://hackernoon.com/improving-text-embeddings-with-large-language-models-analysis-of-training-hyperparameters
Our findings suggest that natural language instructions have a significant impact on performance, as they provide clearer guidance for the model, thereby enhancing the embeddings.
Hackernoonhttps://hackernoon.com/improving-text-embeddings-with-large-language-models-analysis-of-training-hyperparameters
The flexibility of our framework allows customization of text embeddings through instructions, eliminating the necessity for model fine-tuning or rebuilding document indexes.
Hackernoonhttps://hackernoon.com/improving-text-embeddings-with-large-language-models-analysis-of-training-hyperparameters
We observed that while specific configurations like pooling types and LoRA ranks don't greatly affect outcomes, guidance through instructions plays a critical role in embedding efficacy.
Hackernoonhttps://hackernoon.com/improving-text-embeddings-with-large-language-models-analysis-of-training-hyperparameters
Read at Hackernoon
#mistral-7b #llama-2 #text-embeddings #machine-learning #synthetic-data-generation
[
|
]