Generative AI Data Solutions

Human Preference Optimization

Reinforcement Learning from Human Feedback + Direct Preference Optimization

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Advance model capabilities with human preference optimization (HPO), leverage methodologies like reinforcement learning from human feedback (RLHF), and direct preference optimization (DPO) to fine-tune models for real-world performance.

Tech Suranika’s expert humans-in-the-loop help to:

  • Enhance accuracy and relevance
  • Minimize hallucinations
  • Train for edge cases and complex scenarios

What is Human Preference Optimization?

Human Preference Optimization (HPO) is a methodology that combines techniques to align AI models with human expectations and preferences. It leverages structured feedback from human evaluators to enhance the performance, accuracy, and ethical alignment of AI systems.

Two key approaches within HPO are:
Reinforcement Learning from Human Feedback (RLHF)

Refines model behavior through iterative feedback loops and reward systems, teaching models to produce outputs that align with human values and expectations.

Direct Preference Optimization (DPO)

Directly optimizes models by training on ranked human preferences, enhancing performance without requiring complex reinforcement learning setups.

Tech Suranika’s RLHF + DPO Process

Our expert team covers every aspect of your RLHF needs, ensuring consistent, unambiguous responses to empower your models. Here’s how:

1

Precise Feedback

Feedback Types and Reward Systems:

Simple or Complex Reward Systems: In- cludes “thumbs up/thumbs down” and rating scales (0-N).

Nominal Classifications: Such as toxic, stereotypical, copyrighted, hallucinated, etc.

Simple and Complex RLHF: Levels of feed- back detail based on your model’s needs.

Nominal Feedback: Categorizes feedback for easy interpretation and action.

2

Key Success Criteria (KSC) Alignment

Our team defines clear KSCs from the outset to ensure your data aligns with your unique goals and drives your model toward real-world success.

Rigorous Team Selection

We assemble a diverse pool of expert annotators to ensure your data reflects the richness and complexity of true human interaction.

Why Your LLMs Need Human Preference Optimization

Human Preference Optimization (HPO), including both RLHF + DPO, ensures your models meet the highest standards.

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Align Outputs with Human Intent
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Reduce Hallucinations and Improve Accuracy
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Mitigate Bias ad Ensure Ethical AI
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Prepare for Edge Cases and Complex Scenarios
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Optimize for Long-Term Performance

Why Choose Tech Suranika for HPO?

Global Delivery Centers & Language Capabilities
Tech Suranika operates global delivery centers proficient in over 85 native languages and dialects, ensuring comprehensive language coverage for your projects.
Domain Expertise Across Industries
With 5,000+ in-house SMEs covering all major domains from healthcare to finance to legal, Tech Suranika offers expert reinforcement learning from human feedback.
Efficient + Scalable Human Evaluation
We ensure swift, high-quality human evaluation by leveraging our globally distributed teams and industry-leading practices, enabling us to deliver exceptional results at any scale.
Linguist & Taxonomy Specialists
Our team of in-house linguists specialize in creating custom taxonomies and guidelines to optimize generative AI models, ensuring precise and meaningful feedback in the RLHF process.
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