ML Evaluation
Human evaluation infrastructure for ML teams. Collect RLHF preference data, detect hallucinations, evaluate instruction following, assess reasoning chains, and benchmark multimodal models. Scale from internal reviewers to hundreds of recruited evaluators.
LLM Output Comparison
Compare LLM-generated responses side by side. Human reviewers pick winners on criteria like helpfulness, accuracy, or tone. Produces rankings with win rates and agreement scores — essential for RLHF data collection and model selection.
Generated Image Evaluation
Rate AI-generated images for quality, realism, or prompt adherence. Recruit evaluators to score outputs from diffusion models, GANs, or image editors. Get per-image scores with distributions and inter-rater reliability.
TTS Model Benchmarking
Benchmark text-to-speech models with human preference rankings. Recruit listeners to compare voice samples in randomized pairs and produce a ranked leaderboard with statistical confidence and agreement metrics.
Model Selection & RLHF
Collect human preference data for reinforcement learning from human feedback. Compare model outputs at scale with recruited evaluators, producing the pairwise preference datasets needed for reward model training.
Hallucination Detection
Have evaluators flag factual errors, fabricated citations, and unsupported claims in model outputs. Label each response as grounded, partially hallucinated, or fully hallucinated. Critical for measuring factuality in production LLMs.
Instruction Following Evaluation
Evaluate how well models follow complex, multi-constraint instructions. Recruited evaluators rate responses on constraint satisfaction, format adherence, and completeness. Essential for post-training evaluation at frontier labs.
Reasoning Chain Assessment
Have domain experts evaluate step-by-step reasoning traces for logical correctness, completeness, and coherence. Compare reasoning quality across model checkpoints or prompting strategies to guide training decisions.