How TLP Accelerates Large‑Model Data Annotation with Automation

1. Overview of TLP Annotation Platform

TLP is a data annotation platform that supports both large‑model and traditional machine‑learning datasets, handling images, text, video, and audio. It offers a variety of annotation templates and an automatic annotation feature powered by large models to help users complete labeling efficiently.

2. Importance of Data Annotation for Large Models

Data annotation is the process of adding structured labels or comments to raw data, which is essential for building high‑quality training data in machine learning and AI. For large models, the quality of annotated data directly impacts model performance and generalization.

Text annotation: sentiment tags, entity recognition, intent classification.

Image annotation: bounding boxes, segmentation, classification, key‑point labeling.

Audio annotation: keyword, language, or emotion labeling.

Video annotation: timestamped spatial labeling of dynamic objects or actions.

Large models require massive, diverse, and high‑quality annotated data.

3. Specific Requirements of Large‑Model Annotation

Scale: models like GPT or DeepSeek need billions of labeled examples, demanding efficient tools and crowd‑sourced collaboration.

Quality: annotations must be highly accurate to avoid noisy data that can mislead learning.

Diversity: coverage of multiple scenarios, languages, and domains improves generalization.

Consistency: standardized guidelines and review mechanisms ensure uniform labeling across annotators.

4. Creating Annotation Tasks

Users can create tasks for both large‑model and traditional ML labeling. For large models, two dataset types are supported: supervised fine‑tuning (SFT) and reinforcement learning with DPO. Users select templates, assign annotators and reviewers, and configure annotation and review strategies.

During annotation, progress is shown as a percentage, and completed datasets can be saved to a specified location.

5. Annotation Workflow

After a task is created, annotators receive it, click “Start Annotation,” and work on the detailed labeling interface. They can edit the input (question) and output (answer), save, or skip items for later review.

6. Review Process

Reviewers open the review page, click “Start Review,” and either approve or reject each labeled item. The task is considered complete when the progress reaches 100%.

7. Automatic Annotation

To save time, TLP provides an automatic annotation feature. Users select a large model, configure prompt, temperature, and maximum generation length, then trigger “Auto Annotation.” In practice, this can increase labeling efficiency by more than 60% compared with manual effort.