Unlock the potential of your generative AI initiatives by leveraging human-informed feedback loops. The services described below illustrate how this process supports improved training and model refinement.

Improve Model Outputs: Reinforcement Learning from Human Feedback (RLHF)

Source Expertise

Access a network of skilled annotators with domain knowledge across multiple modalities. These specialists help improve the quality of your training datasets.

QC & Data Correction

Model outputs are reviewed and categorized according to custom scoring criteria to identify discrepancies and areas requiring adjustment.

Model Alignment

Align your models’ outputs with defined policies and operational objectives to better support your use cases.

RLHF Automation

Employ a human-in-the-loop workflow combined with automation via a dedicated platform for data pipeline management and model fine-tuning.

Expert Services for Optimizing Accuracy of LLMs & LVMs

Tap into a global network of professional annotators and subject-matter experts whose capabilities adapt as AI evolves. The process supports expert feedback in the form of labels, ratings, corrections, and explanatory commentary throughout the machine-learning lifecycle.

Key capabilities include:

• Recruiting domain experts to work on specific projects.
• Scaling expert-in-the-loop operations across multiple stages of model development.
• Capturing nuanced feedback such as corrective labels, structured ratings, and detailed explanations.

Auditing, Quality Control, and Data Correction

To ensure your models perform as intended, the following functions are applied:

Output Evaluation

Expert teams assess model results and assign scores to measure accuracy and pinpoint where training data may be lacking.

Quality Control

Create clear procedures to check training data quality before teams use it in production.

Data Correction

Review and refine training data and prompts to better align with the expected model behaviour and output quality.

Model Alignment: Collaboration for Interpretability & Performance

By working alongside quality teams and domain experts, you gain tools for:

• Understanding why a model made a given prediction or response (model explainability).
• Evaluating performance using structured scoring systems (e.g., Likert scales) and comparing outputs to intended results.
• Pairing prompts and responses to assess how effectively the model is performing relative to expectations and guiding further prompt design.

RLHF Platform Automation for LLMs, LVMs & Foundation Models

The platform provides multiple features to support the lifecycle of training and fine-tuning:

• Automation of data-pipeline workflows to manage large-scale annotation and model integration tasks.
• Integration of model APIs, plugins and tools into your ecosystem, enabling flexibility for varied use cases.
• Customizable qualitative evaluation metrics to track and quantify model performance.
• Audit-tracking features that capture human feedback along the scoring process for future model iterations.
• Support for annotations across text, audio, video, and image data, plus reporting and analytics for trend-identification.

Next Steps

As the demand for rapid, high-quality annotation increases, combining automated annotation workflows with expert human review helps bring models into production more effectively.