Enhancing LLM Quality Through Expert Response Pair Annotation

Femote subcontracted a fortune 500 company to support a major project that had to do improving the evaluation system for their large language model (LLM). They needed a reliable partner to compare and rate AI-generated responses against prompts with precision and consistency.

The project involved:

• Reviewing a dataset of prompts paired with two AI responses.
• Rating each response based on key metrics

The dataset included over 10,000+ prompt-response pairs, with a strict emphasis on quality and consistency.

• Skilled Annotators: We selected and onboarded a data annotation team experienced in language understanding and AI evaluation.
  
• Followed Set Guidelines: We worked closely with the guidelines set by our client, ensuring consistent rating and ranking of responses.
  
• Layered Quality Control: A thorough review process was implemented to maintain accuracy across all annotations.

• Accuracy: 99% annotation quality based on client evaluations.
  
• Timeliness: Delivered all annotated datasets ahead of the 2-week schedule.
  
• Impact: Helped the client fine-tune their LLM’s ranking models, contributing to better real-world response selection.

The client commended our team for consistent quality and responsiveness throughout the project.

At Femote, we combine skilled human insight with efficient processes to deliver annotation work that makes a measurable difference.