Generative AI Data Solutions
Human Preference Optimization
Reinforcement Learning from Human Feedback + Direct Preference Optimization
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.
Innodata’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.
Innodata’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:
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.
- Multi-Faceted Evaluation: We go beyond simple “thumbs up/thumbs down” by using a detailed feedback system.
- Detailed Response Ratings: Outputs are scored with simple or complex reward systems for granular feedback.
- Classification Based on Key Criteria: We identify issues like toxicity, bias, or plagiarism for targeted improvements.
- Explanatory Feedback: We explain each score with specific details such as factual errors or logical inconsistencies.
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.
Robust Assessment Methodology
Our multi-pass training process ensures the highest quality data by meticulously vetting every response, leaving no room for ambiguity or inconsistency.
Tailored Project Guidelines
We provide clear, documented guidelines to our annotators to objectify subjectivity and cover even the most challenging edge cases, ensuring consistent, reliable data.
Why Your LLMs Need Human Preference Optimization
Human Preference Optimization (HPO), including both RLHF + DPO, ensures your models meet the highest standards.
Align Outputs with Human Intent
Reduce Hallucinations and Improve Accuracy
Mitigate Bias ad Ensure Ethical AI
Prepare for Edge Cases and Complex Scenarios
Optimize for Long-Term Performance
Global Delivery Centers & Language Capabilities
Innodata 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, Innodata 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.
Why
Choose
Innodata
for HPO?
Let’s Innovate Together.
See why seven of the world’s largest tech companies trust Innodata for their AI needs.
We could not have developed the scale of our classifiers without Innodata. I’m unaware of any other partner than Innodata that could have delivered with the speed, volume, accuracy, and flexibility we needed.
Magnificent Seven Program Manager,
Al Research Team
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