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

case_id stringclasses 10 values	domain stringclasses 6 values	question stringclasses 10 values	ai_response stringclasses 10 values	reference_answer stringclasses 10 values	error_category stringclasses 8 values	severity stringclasses 2 values	accuracy_score int64 2 10	reasoning_score int64 2 10	completeness_score int64 2 10	consistency_score int64 2 10	assumption_score int64 2 10	clarity_score int64 8 10	expert_evaluation stringclasses 10 values	overall_score float64 36.5 100
CHEM-001	Organic Chemistry	What is the characteristic stereochemical outcome of an SN2 reaction at a chiral carbon center?	An SN2 reaction proceeds through backside attack by the nucleophile and results in inversion of configuration at the reacting stereocenter.	SN2 reactions involve backside attack and produce inversion of configuration at the reacting carbon.	No Error	null	10	10	9	10	10	10	The response correctly identifies backside attack and stereochemical inversion. The reasoning is chemically consistent and directly addresses the question.	98.5
CHEM-002	Organic Chemistry	Why does an SN2 reaction generally require backside attack?	The nucleophile attacks the substrate from the same side as the leaving group because this provides the shortest path to the carbon center.	The nucleophile attacks from the side opposite the leaving group because this orientation provides the required orbital overlap with the antibonding orbital associated with the C–leaving-group bond and leads to inversion.	Mechanistic Error	Major	2	2	3	7	8	8	The response reverses the fundamental geometry of SN2 attack. Same-side attack is inconsistent with the required backside orbital alignment and the observed stereochemical outcome.	38.5
CHEM-003	Organic Chemistry	What stereochemical change occurs at the reacting carbon in a typical SN2 reaction?	The nucleophile attacks from the backside, but the reacting stereocenter retains its original configuration because the incoming group occupies the same spatial position.	Backside attack in an SN2 reaction produces inversion of configuration at the reacting stereocenter.	Stereochemical Error	Major	3	3	5	5	8	8	The response correctly identifies backside attack but incorrectly concludes that configuration is retained. The key stereochemical consequence of SN2 is inversion.	45
CHEM-004	Quantitative Chemistry	A solution contains 0.50 mol of solute in 2.0 L of solution. What is its molarity?	Molarity is calculated as moles divided by volume in litres. Therefore, 0.50 / 2.0 = 0.25 M.	Molarity = moles / volume in litres = 0.50 mol / 2.0 L = 0.25 mol/L.	No Error	null	10	10	10	10	10	10	The formula, units, calculation, and final answer are correct.	100
CHEM-005	Analytical Chemistry	Can a single IR absorption band always uniquely identify an organic compound?	Yes. Every organic compound has a unique IR absorption band, so identification can normally be made from one strong peak.	No. A single IR absorption band generally cannot uniquely identify an organic compound. Identification normally requires interpretation of multiple characteristic absorptions and, where appropriate, additional analytical data.	Conceptual Error	Major	2	2	3	8	5	9	The response overstates the specificity of a single IR band. IR interpretation relies on patterns of absorptions and should not normally be treated as uniquely identifying a compound from one peak.	37.5
SCI-001	Scientific Reasoning	A laboratory experiment shows a change in color after adding reagent X. What can be concluded solely from this observation?	The color change proves that reagent X caused the intended chemical reaction and confirms that the target product was formed.	The observation demonstrates that a color change occurred after addition of reagent X, but by itself it does not prove the reaction mechanism or confirm formation of the intended product.	Unsupported Assumption	Major	3	2	3	8	2	8	The response treats an observation as definitive proof of causation and product identity. Additional evidence would be required to establish the reaction and confirm the product.	36.5
SCI-002	Scientific Reasoning	If two independent measurements give the same numerical result, does that alone prove that the result is accurate?	Yes. Agreement between two measurements proves that the measured value is accurate.	No. Agreement between measurements can provide evidence of consistency or repeatability, but it does not by itself establish accuracy. Accuracy requires comparison with an appropriate reference, accepted value, or validated measurement approach.	Logical Error	Major	3	2	4	9	3	9	The response incorrectly equates agreement between measurements with accuracy. Repeatability and accuracy are distinct concepts.	41
TECH-001	Technical Reasoning	What should be considered when evaluating the quality of an AI-generated technical response?	The answer should be checked for correctness.	Evaluation should consider technical accuracy, reasoning validity, completeness, internal consistency, assumptions, clarity, and the severity or type of any identified errors.	Missing Information	Moderate	7	6	2	10	8	9	The statement is directionally correct but substantially incomplete. Technical response evaluation should assess multiple dimensions rather than correctness alone.	66
TECH-002	Technical Reasoning	A response states that a process is highly reliable because no failures were observed in a small number of trials. Is that conclusion justified?	The process is highly reliable because no failures occurred. However, the small sample size means that the absence of failures may not be sufficient evidence to establish reliability.	The absence of observed failures in a small sample does not by itself establish high reliability. The conclusion should consider sample size, study design, variability, and the level of evidence required.	Internal Inconsistency	Moderate	5	5	6	2	6	8	The response first makes a strong reliability claim and then immediately acknowledges that the available evidence may not support that claim. The contradictory conclusions reduce internal consistency.	52.5
COMP-001	Comparative Evaluation	Which response demonstrates stronger technical reasoning when evaluating an experimental observation?	Response A: The observation proves the proposed mechanism. Response B: The observation is consistent with the proposed mechanism, but additional evidence is required to distinguish it from alternative explanations. Response B is stronger because it separates the observation from the strength of the causal conclusion.	Response B demonstrates stronger reasoning because it recognizes that an observation may support a hypothesis without uniquely proving the proposed explanation.	No Error	null	10	10	10	10	10	10	The comparison appropriately distinguishes evidence from proof and explains why the more qualified interpretation represents stronger scientific reasoning.	100

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:

Final-answer accuracy
Reasoning validity
Technical completeness
Internal consistency
Assumption handling
Technical clarity
Error type and severity
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

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