A frontier AI lab needed to improve and evaluate the factuality and response grounding of a next-gen LLM across a wide knowledge surface. Traditional hallucination tagging wasn’t enough, and the team required:

• Prompt-level claim scoring across diverse user intent types
• Web and social media source evaluation with real-time news volatility
• Grounded feedback loops to refine instruction-following and content accuracy
• A dataset to help reduce “lazy responses” and language errors
• A rubric that would work across factual, derived, and unverifiable responses, not just binary yes/no grading

Turing was asked to build a scalable, human-in-the-loop eval pipeline that could surface both systemic weaknesses and fine-grained factual mismatches across categories like coding, politics, science, technology, entertainment, and economics.

Dataset & taxonomy

We built a multi-dimensional dataset covering:

• 150+ categories and subcategories, including Politics, Global Events, Religion, Sports, History, Law, Tech, and more
• Balanced prompt types: declarative claims, open questions, and opinion-contextualized queries
• 5000+ prompts, each with:
  a. Model response
  b. Supporting web sources
  c. Factuality judgments (per claim)
  d. Source helpfulness, relevance, completeness
  e. Ideal response rewrites for RLHF fine-tuning

Evaluation & QA pipeline

We designed and executed a rigorous rubric covering:

To standardize outputs, we implemented:

• A five-tier factuality error schema (e.g., outdated, chronological, data misinterpretation, unverifiable, fabricated)
• Instruction-level improvement suggestions + rewrites used as ideal fine-tuning targets
• Blind A/B testing across three models to detect systemic failure patterns

Qualitative analysis

We also performed a qualitative analysis of model behavior across categories like Business, Politics, and Science. We flagged:

• Unverifiable extrapolations (e.g., conclusions from vague social posts)
• Over-indexing on noisy source snippets
• Incomplete use of helpful data
• Hallucinated citations and pretextual knowledge injections
• Language verbosity and structural bloat in “lazy” outputs

We evaluated three instruction-following models:

• Model A (fine-tuned with Turing data)
• Model B (an earlier baseline), and 
• Model C (a publicly available SOTA model)

across 500+ prompts using blind human review, ensuring that evaluators had no visibility into which model they were assessing. This allowed for objective, model-agnostic scoring across all five rubric dimensions.

Model A delivered the highest percentage of net positive responses, indicating improved clarity, completeness, and structure in output.

While Model C led in factuality, Model A showed comparable performance and reduced hallucination risk.

Model A demonstrated disciplined use of RAG sources, minimizing overuse or irrelevant citations while achieving high grounding coverage.

Model A had the most consistent behavioral output, with the lowest incidence of grammar issues, hallucinations, and over-verbose responses.

Model A led in categories like Business, History, Entertainment, and Literature. Model C performed strongest in STEM-heavy categories like Science and Technology, while Model B showed strengths in Art and Politics.

Here are the key takeaways:

• 81.6% net positive response quality, outperforming other frontier models in blind evals
• 95.2% factuality rate across human-reviewed samples, matching or exceeding SOTA models
• 59.6% optimal RAG source utilization, with minimal overuse or distraction from context
• Category-level wins in Business, History, Entertainment, and Literature, where the model achieved both high response quality and factual precision
• Highest expected model behavior and lowest rate of grammar or formatting issues among peers

This benchmark study demonstrated that AI models, when fine-tuned with human-labeled QA data, can outperform frontier models on several grounded response metrics:

• Train: The lab can now improve models on web-based response generation
• Evaluate: Researchers can assess derivability, not just factuality, using blind human rubrics
• Diagnose: Behavioral metrics like "lazy response" or "missing key info" offer fine-tuning levers
• Extend: Frameworks can be adapted to code, logic, and visual reasoning benchmarks