TL;DR

Large language models (LLMs) like ChatGPT don’t arise out of thin air — they’re built on mountains of carefully labeled text. Annotators tag entities, categorize sentiments, rank responses and provide preference judgments so that models learn to understand and follow human intent. Reinforcement learning from human feedback (RLHF) takes this a step further by collecting pairwise preference data and aligning models to human values. Although generative tools can assist, human expertise remains essential for creating, reviewing and curating high-quality data. Annotation doesn’t just improve AI — it creates remote work opportunities and helps ensure safe, fair and reliable systems.

Large language models rely on vast libraries of labeled text to behave helpfully and responsibly. In this guide, we explain what LLMs are, why they depend on human-labeled data, how RLHF shapes their behaviour, what kinds of annotation tasks go into training them, and what challenges and opportunities await annotators in the era of generative AI.

What Are Large Language Models?

At the heart of today’s AI boom are large language models — deep neural networks trained on billions of words to generate, summarize and translate human language. These models learn the statistical patterns of text by predicting the next word in a sequence and then refine their abilities through supervised fine-tuning and reinforcement learning. ChatGPT, Claude and other assistants you interact with every day are built on these foundations. Their power comes from scale: massive parameter counts, diverse training corpora and significant compute allow LLMs to capture grammar, world knowledge and nuance. But this raw ability isn’t enough to follow instructions or align with social expectations; that’s where labeled data and human feedback enter the picture.

Why LLMs Need Human-Labeled Data

Unlabeled web text teaches a model to predict likely words, but it doesn’t show the model how to solve concrete tasks. To answer questions, detect spam or translate between languages, developers rely on curated datasets where human annotators assign task-specific labels. Text annotation adds the context that neural networks need to distinguish sentiment, topics or entities.

  • Categorization: Classify raw data for tasks like sentiment analysis or spam detection.
  • Context labeling: Add intermediate labels that convey tone, topic or nuance.
  • Confidence scoring: Assign reliability scores to help models weigh uncertain examples.
  • Preference alignment: Label responses or outputs according to which ones better match human intent.
  • Relation annotation: Mark relationships between entities or events in a passage.
  • Semantic roles: Tag the roles words play in a sentence, such as subjects, objects or actions.
  • Temporal sequencing: Tag timelines and steps in stories, instructions or dialogues.
  • Synthesize pairs: Create instruction–response examples and synthetic text to expand training data.
Why it matters

High-quality annotation is especially important for underrepresented languages, specialised domains and sensitive use cases. Accurate labels help models avoid perpetuating stereotypes or misunderstandings. Manual labeling ensures that voice-assistants understand regional slang, chatbots recognize nuanced emotions and summarization systems capture the gist without hallucinating facts.

Reinforcement Learning From Human Feedback

Once a base model has been trained on annotated tasks, it still needs to learn how to respond in a way that feels natural, helpful and aligned with human values. Reinforcement learning from human feedback (RLHF) fills this gap. In this technique, annotators compare pairs of model outputs and select the one that better follows an instruction. Those preferences are used to train a reward model, which in turn fine-tunes the base model via reinforcement learning.

The RLHF book notes that this approach was “crucial to the success of the first ChatGPT” and that companies which embraced RLHF early on “ended up winning out.” Collecting preference data is expensive — budgets can run from tens of thousands to millions of dollars — but the payoff is a system that more reliably follows instructions, rejects harmful content and mirrors human preferences. Open source projects such as Zephyr and Tülu demonstrate how communities are iterating on RLHF with Direct Preference Optimization and other post-training techniques.

Tip

To contribute to RLHF projects, try ranking model outputs in annotation tasks. Your judgments help train reward models that make AI assistants more polite, concise and helpful. Platforms like Annota offer opportunities to participate in these remote preference-ranking jobs.

Key Text Annotation Tasks for LLM Training

LLMs learn by example, and the quality of those examples depends on the variety of tasks captured in your dataset. Here are some of the core annotation tasks used to teach language models how to interpret and respond to text:

  • Sentiment & classification: Label reviews or posts as positive, negative or neutral to teach models tone and opinion.
  • Named-entity recognition (NER): Highlight people, organisations, products or locations so models can identify and link entities correctly.
  • Part-of-speech & syntax: Tag nouns, verbs and other grammatical elements to provide structure and enable parsing.
  • Topic & intent classification: Assign topics to passages and classify user intent to help models understand what a request is about.
  • Relation & reasoning: Annotate relationships between entities (e.g., “Alice works at Company X”) and mark steps in reasoning chains.
  • Instruction–response pairs: Create high-quality prompts paired with expert answers for instruction tuning and training helpful assistants.
  • Preference & ranking: Compare pairs of model responses and label which one better follows the instruction, as in RLHF.

Challenges and Ethical Considerations

Annotating text for large language models is not without obstacles. Key concerns include:

  • Bias & fairness: Models and annotators can reinforce stereotypes if labels aren’t inclusive and representative.
  • Quality & reliability: Without clear standards and oversight, annotations may be inconsistent or erroneous.
  • Cost & fatigue: RLHF and manual annotation are resource-intensive. Long hours lead to burnout and mistakes.
  • Privacy & security: Annotators sometimes handle sensitive or personal information, so data must be protected and anonymised.
Ethics in practice

To minimise harm, ensure datasets include diverse perspectives and train annotators to recognise their own biases. Break work into manageable batches, provide clear guidelines and support, and enforce strict privacy protocols.

1. Multi-Stage Post-Training

Instruction tuning, RLHF and Direct Preference Optimization are evolving into multi-stage pipelines that combine labelled datasets, preference ranking and reasoning tasks. As methods like constitutional AI emerge, annotators will be needed to refine ethical guidelines and evaluate model reasoning.

2. Automation & Synthetic Data

Researchers are experimenting with automated annotation pipelines and synthetic data generation to reduce reliance on costly human feedback. Generative models can pre-label data, but human oversight remains essential to catch errors, bias and hallucinations. Synthetic datasets amplify real examples but must be curated carefully.

3. Community & Open Source

Open projects like Zephyr and Tülu show how crowdsourced RLHF and annotation can drive rapid progress. Communities share datasets, reward models and best practices, lowering barriers to entry and creating transparent benchmarks.

4. Remote Work Opportunities

The growth of instruction tuning and RLHF has increased demand for part-time, remote annotation work. Platforms like Annota connect skilled workers with projects that directly influence how future AI systems behave. Whether you’re a linguist, domain expert or careful reader, there’s an opportunity to contribute.

Best Practices for Annotators

Great annotation follows a few simple rules. Follow these guidelines to ensure consistency and fairness:

  • Develop clear guidelines: Provide annotators with precise instructions, examples and edge-case explanations.
  • Work in manageable batches: Take breaks to avoid fatigue and ask questions when unsure.
  • Collaborate & calibrate: Hold calibration sessions, spot checks and consensus reviews to align understanding.
  • Document decisions: Keep a codebook and note rationales to make the process transparent and repeatable.
  • Stay ethical & secure: Recognise potential bias, strive for neutral labels and protect sensitive information.

Conclusion

The rise of large language models has transformed our digital lives, but these systems wouldn’t exist without the painstaking work of text annotators and the guiding hand of human feedback. Annotated datasets teach LLMs how to understand and follow instructions, while RLHF aligns them with our values. As the field advances, opportunities for meaningful, flexible work in data annotation will only expand. Annota is proud to support this ecosystem by connecting annotators with projects that matter. If you’re ready to help shape the future of AI, explore opportunities on Annota.work today.

Share: X LinkedIn Email