- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
CommPath LLM vs. Preview LLM
- CommPath LLM
- Preview LLM
Below is an outline of some of the considerations when deciding on an LLM to use. If your use case requires extracting more than 30 fields per message, we currently recommend using the Preview LLM.
- Leverages UiPath’s® proprietary LLM, fine-tuned for Communications data.
- Currently limited to extracting approx. 30 fields per message.
- Less latency than the Preview LLM.
- Can be fine-tuned based on your data.
- Improving performance for CommPath, both in terms of the number of fields which can be extracted and the inference speed for the model is a high priority for 2024.
- Higher occurrence confidences (check the Automating with GenEx section for more details) compared to the Preview LLM.
- Leverages Azure OpenAI’s GPT model as the underlying LLM.
- UiPath® cannot guarantee uptime, as this is entirely dependent on the Azure OpenAI endpoints. If the endpoints are down or overloaded, UiPath® cannot guarantee availability.
- You can extract more than 30 fields per message.
- Higher amount of latency compared to CommPath LLM.
- Limited to in-context learning.
Note: When using in-context learning, the platform can only learn from what you prompt it with. Communications Mining can automatically refine the prompt to an extent, but the model doesn't learn from any user-led validation.
Use the settings illustrated below, to select which LLM you want to use for the Generative Extraction.
CommPath LLM is enabled by default. To enable the Preview LLM, the toggles from the following image are required.
If the Use preview Generative Extraction model toggle is turned off, it means that you are using the CommPath LLM.
Having the Use generative AI features and Use preview Generative Extraction model toggles turned on means the platform uses the UiPath® Azure OpenAI endpoint in the extraction process.
- Start training your extractions with the CommPath LLM.
- If the extractions extract correctly, continue to train the extractions using the CommPath LLM. If not, due to high number of fields or large tables in each message, switch to the Preview LLM.
To determine if your extractions predict correctly, check the validation statistics in the Generative Extraction tab, on the Validation page. If the precision and recall of the extractions are appropriate for your use case, continue to use the CommPath LLM.
If any data points don't extract as expected with the CommPath LLM:
- Pin the current model version by going to models and select pin on the most recent model version.
- Reach out to your UiPath® Representative, making note of the model version where the extractions were not performing well on. Your UiPath® Representative will work directly with the Communications Mining product team to investigate and implement improvements.
- If you use the Preview LLM, continue to train your model the same way you trained the CommPath LLM. Go through it, and provide correct examples for each of your extractions.
