How LLMs can be used to enhance the depth and accuracy of annotations attached to data?

Data annotation with human-in-the-loop has its limitations because the annotator's domain knowledge bounds his perception of data. Hence, the diversity of data and labels demands additional expert assistance.

LLMs trained on huge datasets can be very helpful when adding depth to annotations. They can process the data better than the human eye.

This is essential because people building the knowledge economy, say AI scientists, should have access to data that is annotated in-depth, irrespective of the barrier mentioned above.

Only then can the AI scientist train his model with accuracy. Only then can one see the limitless potential of data in action i.e. models whose results are promising.

LLMs such as Gemini have been integrated with data annotation platforms such as Labellerr to speed up the data annotation process. Not just speed up. It also adds accuracy and depth to the annotations.

Gemini API, configured to send responses to the UI of the platform, sends measured analysis of data that has been integrated with Labellerr. ( The user has to enter the right question in the prompt window ).

Measured because there is a safety function that blocks responses to questions that are not ethical.

Also, there is a prompt function defined at the backend to send responses in a specified format.

Gemini in action can be seen below for three kinds of data.

Classifications can be added to an image showing a snapshot of moves by some cricket players. The classification generated by Gemini is accurate to the point that "weight distribution" while executing a move such as a drive shot by a cricketer is also listed in the caption answer window. What more? You can export such intelligent data to train your AI models.

An AI scientist at a company, working on a product that wants to use voice technology to help customers interact with a service, say food ordering, wants annotated voice samples.

Labellerr's audio analyser would help the AI scientist obtain an accurate transcription of the audio recording. Also, the AI scientist can obtain the essence of the conversation, the next steps after the conversation, and things like that.

What more? Such annotated, intelligent audio data can be exported from Labellerr for the training of the AI model that powers the voice technology.

Below is a sample audio file analysed by Labellerr's Audio analyser.

Last but not least, lengthy text data can be annotated with ease through Gemini. A sample piece of text has been summarised with Gemini as shown below. Such classifications of text data, generated by Gemini add depth and accuracy to text data.

Accurate AI models stand on the pedestal of such annotated data. This will also ease the burden of the AI scientist's data-prep work as such classifications are less likely prone to error.

The needs of AI scientists for accurately labelled data will grow as the standards on which a model's outcomes are judged, become more stringent.

It is not just about models anymore. It is about accurate models whose predictions look more promising. Annotating data with sufficient generosity in adding depth is the little step that can be taken to compound the benefits obtained from the models.