CQA-pharma/cqa-ai-technical-response-evaluation

CQA AI Technical Response Evaluation Dataset

Overview

This is a synthetic dataset designed to demonstrate structured evaluation of AI-generated technical responses.

The dataset evaluates technical responses beyond a simple correct/incorrect classification by considering multiple dimensions of response quality, including accuracy, reasoning validity, completeness, consistency, assumption handling, clarity, error classification, severity, and expert evaluation.

Purpose

The dataset is intended to support:

• —AI-generated response evaluation
• —Technical reasoning assessment
• —Error detection
• —Response-quality analysis
• —AI quality-assurance methodology
• —Evaluation framework development
• —Demonstration of multidimensional technical-response assessment

Dataset Structure

Each evaluation case contains:

• —Case identifier
• —Technical domain
• —Technical question
• —AI-generated response
• —Reference answer
• —Error category
• —Error severity
• —Accuracy score
• —Reasoning score
• —Completeness score
• —Consistency score
• —Assumption-handling score
• —Clarity score
• —Expert evaluation
• —Weighted overall response-quality score

Evaluation Dimensions

The evaluation framework considers the following dimensions:

1. 1.Final-answer accuracy
2. 2.Reasoning validity
3. 3.Technical completeness
4. 4.Internal consistency
5. 5.Assumption handling
6. 6.Technical clarity
7. 7.Error type and severity
8. 8.Expert assessment

Error Categories

The synthetic evaluation cases represent several technical response conditions, including:

• —No Error
• —Mechanistic Error
• —Stereochemical Error
• —Conceptual Error
• —Unsupported Assumption
• —Logical Error
• —Missing Information
• —Internal Inconsistency

Error Severity

Responses are categorized according to the identified issue:

• —None
• —Moderate
• —Major

Dataset Composition

The current dataset contains 10 synthetic evaluation cases covering multiple technical domains, including:

• —Organic Chemistry
• —Quantitative Chemistry
• —Analytical Chemistry
• —Scientific Reasoning
• —Technical Reasoning
• —Comparative Evaluation

Scoring

The dataset contains individual evaluation dimensions scored on a structured scale and a weighted overall response-quality score.

The overall score is intended to provide a multidimensional representation of response quality rather than treating correctness as a binary outcome.

Data Quality

The dataset contains no missing values in the evaluated fields.

The examples include both technically sound and technically flawed responses to demonstrate the evaluation methodology.

Intended Use

This dataset may be used for:

• —Research and experimentation
• —AI evaluation methodology
• —Technical reasoning analysis
• —Educational demonstrations
• —Quality-assurance framework development
• —Benchmark prototyping
• —Response evaluation system development

Limitations

This is a small synthetic dataset intended primarily for methodological demonstration.

It should not be interpreted as a statistically representative benchmark of real-world AI performance.

Results obtained from this dataset should not be generalized to production AI systems without additional validation using larger, independently curated datasets.

Data Generation

The examples are synthetic and were constructed specifically to represent different technical-response quality conditions.

No confidential, proprietary, employer-specific, personal, or restricted information is included.

Repository Contents

evaluation_dataset.csv
README.md