Enhancing LLM Accuracy Through Our Human-in-the- Loop Data Annotation

Challenge

The client’s large language models (LLMs) were producing a significant number of inaccurate and biased responses due to hallucinations (errors). Furthermore, the client wished to allocate their internal resources towards training and tuning their LLMs, rather than focusing on writing prompts and benchmarking responses. They concluded that outsourcing this task to experts would be more efficient and effective.

DDD’s Solution

Our solution involved creating a benchmark dataset for the client’s LLMs, focused on training and tuning quality. We achieved this through human-in-the-loop responses, which were derived from our own HITL-generated prompts. With a list of domain-specific topics provided by the client (e.g., Data Science, History, Accounting), our writers crafted prompts and responses using their deep domain expertise and knowledge.

Providing the Client with Quality Prompts and Responses That Minimized Hallucinations and Enhanced Performance

Impact

We delivered a comprehensive set of prompts in the proper syntax and style for each domain with factual responses. These resources enabled the client to easily verify, train, and tune their LLMs through a structured validation process.

This approach significantly reduced the occurrence of hallucinations and biased responses, leading to improved prompt responses, enhanced model performance, and an overall better user experience (UX).