LLM Training Data & Evaluation
The fastest-growing part of our text work — human data for teams building or adapting language models:
Instruction & Fine-Tuning Datasets
Writing and curating prompt-response pairs to your data specification: task coverage, tone, format, length, and refusal behaviour. Used for supervised fine-tuning of general and domain-specific models.
Preference Ranking (RLHF)
Comparing model responses side by side and ranking them against defined criteria — helpfulness, accuracy, tone, safety. Calibrated raters and tracked inter-rater agreement give your reward model a preference signal you can trust.
Model Output Evaluation
Rubric-based scoring of model outputs for accuracy, coherence, groundedness, and policy compliance. Used for regression testing between model versions, benchmark construction, and ongoing quality monitoring.
Red-Teaming & Safety Labeling
Writing adversarial prompts, flagging unsafe or policy-violating outputs, and labeling harm categories. Supports safety training and pre-launch evaluation.
Text Annotation Methods
Our teams handle the full range of text annotation tasks:
Named Entity Recognition (NER)
Identifying and classifying named entities in text — persons, organizations, locations, dates, monetary values, product names, and custom entity types. Essential for information extraction, knowledge graph construction, and structured data generation from unstructured text.
Text Classification
Assigning predefined categories or labels to text documents, paragraphs, or sentences. Used for topic classification, content categorization, spam detection, and document routing.
Sentiment Analysis Labeling
Annotating text with sentiment scores or categories (positive, negative, neutral) at document, sentence, or aspect level. Supports brand monitoring, customer feedback analysis, and social media analytics.
Intent Detection
Labeling user messages or queries with the intended action or purpose. Critical for chatbot training, virtual assistant development, and conversational AI systems.
Identifying and labeling relationships between entities in text — for example, “Company X acquired Company Y” or “Person A is the CEO of Organization B.” Used for knowledge graph construction and information extraction.
Coreference Resolution
Linking pronouns and references to the entities they refer to within text. Supports reading comprehension models and advanced NLP systems.
Text Summarization Evaluation
Rating and comparing machine-generated summaries for coherence, accuracy, and relevance. Used to train and evaluate text summarization models.
Applications
Our text annotation services support a wide range of NLP and LLM applications:
- LLM fine-tuning and alignment — Instruction datasets, preference ranking, safety labeling
- Model evaluation — Rubric-based output scoring, version regression testing, benchmark construction
- Chatbot and virtual assistant training — Intent labeling, entity extraction, dialogue annotation
- Search relevance — Query-document relevance scoring, search result ranking
- Content moderation — Toxicity labeling, policy violation detection, content categorization
- Customer feedback analysis — Sentiment labeling, topic classification, key phrase extraction
- Legal and compliance — Contract clause classification, regulatory document analysis
- Healthcare NLP — Clinical note annotation, medical entity recognition, ICD coding support
Quality Assurance
Text annotation demands careful attention to context and guidelines. Our QA process includes:
- Detailed annotation guidelines developed in collaboration with your NLP team
- Annotator qualification tests before production work begins
- Inter-annotator agreement metrics to measure consistency
- Senior reviewer audits on a defined sampling basis
- Iterative guideline refinement based on edge cases and annotator feedback
