Evaluating Complex AI Responses Across 14,000+ STEM and Coding Tasks
Evaluated multi-turn and single-turn tasks for a high-signal evaluation dataset. Each task involved comparing two model completions on complex scientific, mathematical, or coding queries and required raters to analyze correctness, instruction following, hallucination risks, and reasoning structure.
14,000+
model ranking tasks evaluated spanning STEM, coding, and assistant-style prompts.
100%
acceptance rate across all submissions.
6+/7
average rating on a 7-point quality scale.
The Challenge
The client needed high-difficulty tasks annotated with expert-level comparisons across completion quality, correctness, clarity, and user alignment. These evaluations would:
- Feed reward model and preference model training pipelines
- Surface consistent gaps in reasoning, code logic, or factual precision
- Require tight alignment with highly specific evaluation rubrics
- Maintain consistency and depth across thousands of rounds
The Approach
Turing deployed a senior expert QA team composed of subject-matter specialists with master’s or PhD degree in coding, mathematics, science, physics, and chemistry. Each reviewer had extensive experience in LLM evaluation and instruction-following assessment.
Each task followed a standardized protocol:
Evaluation criteria
Experts assessed paired completions on:
- Correctness: factual accuracy, logical soundness, and output validity
- Instruction following: prompt alignment, constraint adherence, tone
- Hallucination avoidance: grounding in sourceable, verifiable facts
- Comprehensiveness and brevity: Is the response too verbose, or missing key logic?
- Clarity, structure, and LaTeX rendering where applicable
Experts also provided:
- Time-constrained preferences such as which response would help the user more quickly
- Major error tags and improvement suggestions
- Step-by-step comparison rationales with bullet-pointed checks and failure modes
Workflow & quality standards
- All responses were reviewed using client-issued rubrics
- Rationale quality and rating logic were calibrated through internal review loops
- Workflows maintained high throughput without compromising judgment depth
Key Results
- Evaluated more than 14,000 difficult prompt-response pairs with structured scoring fields
- Maintained 100% task acceptance and average quality rating above six out of seven
- Delivered detailed rationales covering factual, structural, and stylistic issues
- Flagged hallucinations, subtle errors, and time-wasting formats in completions
The Outcome
Turing’s contributions helped the client:
- Benchmark model preference across correctness, hallucination, and instruction adherence
- Capture nuanced differences in LLM outputs on open-ended, multi-turn tasks
- Tune reward models using expert-aligned rankings and structured QA metadata
- Scale evaluations across math, logic, code, and scientific writing with confidence in human supervision
Want to train or evaluate models using expert comparison data?
Request a sample with a user prompt and two model completions, expert ranking with rationale, major error tags, numeric scores, and improvement notes.
Request SampleFAQ
What types of questions were evaluated?
Multi-domain queries across STEM, coding, physics, and chemistry.
What’s considered a “major error”?
Factually incorrect claims, logic flaws, execution errors, hallucinated sources, or failure to follow core instructions.
What’s included in each task?
Each task includes two model completions, expert ranking, written rationale, a score from one to seven, error tagging, and feedback notes.
Can this dataset be used for model training or eval?
Yes. The dataset supports preference modeling, reward tuning, and fine-grained performance audits.
What’s the NDA process?
A standard mutual NDA. Turing provides the countersigned agreement within one business day.
How fast can I get a sample?
Within three business days after NDA execution.
Need structured human evaluations across STEM, code, and logic?
Request a dataset with annotated completions tagged for instruction following, correctness, and reasoning gaps.