Accelerating LLM Development & Fine-Tuning

Advancing the fluency & accuracy of Generative AI with Big Tech.

Diving into the realm of generative AI (GenAI), a large foundational model developer teamed up with Welocalize to improve the accuracy and fluency of large language model (LLM) output. Amidst soaring demand and a highly competitive landscape in 2023, they achieved the following:

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The Challenge

Due to the growing demand for advanced LLM models, the client urgently required a mix of cultural adaptation, supervised fine-tuning (SFT) data, and reinforcement learning from human feedback (RLHF) services to improve the accuracy, fluency, and safety of their LLM outputs. The challenges included:

The Solution

To tackle these challenges, Welocalize quickly trained and deployed remote workers across 35+ locations to address diverse data needs in 5 categories:

  1. Input Evaluation: Adaptation and rating of LLM prompts, ensuring the locale-specific quality of LLM output
  2. Fact Verification: Meticulous review and annotation of LLM factual output for accuracy
  3. Fluency Review: Assessment and scoring of LLM outputs based on linguistic and cultural acceptance criteria
  4. Open Writing & Output Drafting: Rewriting and enhancing LLM outputs that did not meet the acceptance threshold, aiding in model fine-tuning
  5. Model Output Evaluation: Grading of LLM output that helps the model understand the quality of results, and improve reward function

By addressing the client’s immediate needs, we adopted a strategy characterized by flexibility and speed in ramp-up. This approach entailed agreeing to premium pay rates and additional PM fees, enabling the swift onboarding of a vast pool of skilled workers.

To effectively meet the unpredictable demand and ensure reliable 12–24 hour turnarounds, a significant emphasis was placed on assembling a substantial team capable of efficiently handling peak demands.

Additionally, the well-being of team members was prioritized by implementing a robust protocol that would inform them about the nature of the content they would be assessing and gain their consent before exposing them to sensitive material. This approach ensured their safety and fostered a safe working environment.

The Results

The outcomes of this collaboration were truly noteworthy. Over 9,500 remote workers, proficient in LLM evaluation workflows, were mobilized, marking a significant stride in resource management and utilization.

This expansive team facilitated extensive coverage across more than 35 locales globally, catering to diverse linguistic and cultural nuances.

The successful management and timely completion of a wide array of tasks were pivotal in contributing to the refinement and enhancement of LLM models, showcasing the efficacy and adaptability of the partnership in navigating the complexities of generative AI.

“This partnership exemplified the rapid and flexible solutions that can be achieved and highlighted the commitment to quality, worker well-being, and adaptability in the face of diverse challenges. As GenAI continues to evolve, this collaboration serves as a beacon of innovation and excellence in LLM development and fine-tuning.”

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