To evaluate the results, the standard metrics are useful
Posted: Sat Jan 25, 2025 4:34 am
processing can be a bit more complicated, for example when Regex (regular expressions) need to be used to process the extracted data.
Overall, the choice of method for extracting keywords and information from texts depends on the specific requirements and resources. By testing and comparing different approaches, you can find and optimize the best method for your application.
Conclusion on the extraction of single or multiple keywords
However, the recommendation is to extract many terms as JSON or as a table, as this usually allows you to keep the cost per page at around one to two cents or even cheaper.
Explain is a new feature of Aleph Alpha that aims to solve the indonesia consumer email list problem of hallucinations of Large Language Models (LLMs). LLMs tend to invent information or lie when they don't know exactly how to respond to a query. Explain offers a solution to this problem by giving users the ability to detect whether the information generated by the LLM comes from the text or not.
evaluation and iteration
To be able to optimize the prompt efficiently, it is advisable to have a small, diverse dataset consisting of 10 to 30 documents. Of course, integrated into a small pipeline, this allows for rapid iteration and initial testing, making it easier to identify problems and adapt the prompt without spending a lot of time. Once the evaluation on the smaller dataset produces satisfactory results, it is advisable to scale the test to a larger dataset (50+) to check the performance of the model under more realistic conditions. It is important to emphasize that we often talk about adapting the models to a domain or fine-tuning.
In practice, however, this is often not necessary, as instructions and examples can be used to introduce the model to the context and domain without the need for fine-tuning. Fine-tuning can be problematic, as it typically costs between 10,000 and 250,000 euros and must be hosted by the provider of the Large Language Models (LLMs), which means additional inference costs. This is less advantageous in terms of both scalability and cost. Therefore, optimizing prompts using instructions and examples should be considered as a more efficient and cost-effective alternative.
Accuracy measures the ratio of correctly predicted outcomes to the total number of predictions.
Precision is the ratio of true positives to the sum of true positives and false positives, while recall is the ratio of true positives to the sum of true positives and false negatives. These metrics are particularly useful when the classes in a dataset are imbalanced or the cost of incorrect predictions varies.
Overall, the choice of method for extracting keywords and information from texts depends on the specific requirements and resources. By testing and comparing different approaches, you can find and optimize the best method for your application.
Conclusion on the extraction of single or multiple keywords
However, the recommendation is to extract many terms as JSON or as a table, as this usually allows you to keep the cost per page at around one to two cents or even cheaper.
Explain is a new feature of Aleph Alpha that aims to solve the indonesia consumer email list problem of hallucinations of Large Language Models (LLMs). LLMs tend to invent information or lie when they don't know exactly how to respond to a query. Explain offers a solution to this problem by giving users the ability to detect whether the information generated by the LLM comes from the text or not.
evaluation and iteration
To be able to optimize the prompt efficiently, it is advisable to have a small, diverse dataset consisting of 10 to 30 documents. Of course, integrated into a small pipeline, this allows for rapid iteration and initial testing, making it easier to identify problems and adapt the prompt without spending a lot of time. Once the evaluation on the smaller dataset produces satisfactory results, it is advisable to scale the test to a larger dataset (50+) to check the performance of the model under more realistic conditions. It is important to emphasize that we often talk about adapting the models to a domain or fine-tuning.
In practice, however, this is often not necessary, as instructions and examples can be used to introduce the model to the context and domain without the need for fine-tuning. Fine-tuning can be problematic, as it typically costs between 10,000 and 250,000 euros and must be hosted by the provider of the Large Language Models (LLMs), which means additional inference costs. This is less advantageous in terms of both scalability and cost. Therefore, optimizing prompts using instructions and examples should be considered as a more efficient and cost-effective alternative.
Accuracy measures the ratio of correctly predicted outcomes to the total number of predictions.
Precision is the ratio of true positives to the sum of true positives and false positives, while recall is the ratio of true positives to the sum of true positives and false negatives. These metrics are particularly useful when the classes in a dataset are imbalanced or the cost of incorrect predictions varies.