Frontier Alignment

Supervised Fine-Tuning Demonstrations

High-quality SFT demonstrations for large language models: human-written ideal responses, instruction-following datasets, and fine-tuning pipelines at scale.

Supervised fine-tuning is how capable base models become useful, domain-aligned tools. The quality of SFT demonstrations, the human-authored examples that define ideal model behaviour, is the most direct lever teams have over what their model learns to do and how it learns to do it.

Appen produces supervised fine-tuning demonstration data across instruction following, task completion, domain-specific reasoning, and multi-turn dialogue at the quality, diversity, and scale that frontier alignment demands.

What Appen Delivers

Expert Demonstration Writing

Human-authored ideal responses written by contributors matched to the task domain, calibrated to the quality bar your model requires. Demonstrations cover simple instruction following through to complex multi-step technical tasks, with every example reviewed against output specifications before entering your training pipeline.

Instruction Diversity and Coverage

Systematic prompt diversity across task type, difficulty level, domain, and linguistic register to ensure your fine-tuned model generalises rather than overfits to a narrow distribution. Appen's dataset design teams build coverage specifications before collection begins, not after.

Format and Schema Compliance

Demonstrations structured to your exact output format requirements, whether that is JSON schemas, structured markdown, code with explanations, or any custom format your model architecture and downstream application requires.

SFT as the Foundation of Alignment

Before RLHF can refine a model's outputs, SFT must establish the baseline of correct behaviour. Well-constructed demonstration data is what determines whether RLHF improves a model that is already mostly right, or struggles to correct one that is consistently wrong.

For teams building on base models or adapting general models to specialist domains, SFT demonstration quality is the highest-leverage investment in the alignment pipeline. Appen's programmes are designed to maximise that leverage.

Related Resources

Case Study

Improving Multilingual LLM Performance with Supervised Fine-Tuning

How a leading technology improved multilingual LLM performance across 70 dialects with human preference rankings and supervised fine-tuning.

Case study

Blog

Unlocking the Power of Human Feedback: Benefits of RLHF

Reinforcement learning with human feedback (RLHF) is a cutting-edge technique that has been gaining popularity in recent years as a means of improving the performance of large language models.

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