Blame for Readme.md

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pretty_name: General Knowledge Benchmark (GKB-1000)
license: mit
language:

* en
    task_categories:
* question-answering
* text-generation
    tags:
* benchmark
* general-knowledge
* ai-evaluation
* model-evaluation
* logical-reasoning
* mathematical-reasoning
* scientific-knowledge
* technical-knowledge
* language-understanding
* agentic-reasoning
* critical-thinking
* reliability
    size_categories:
* 1K<n<10K

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General Knowledge Benchmark (GKB-1000)

Version: 1.0
Dataset size: 1,000 questions
Difficulty: Advanced
Format: JSONL
Language: English

Overview

The General Knowledge Benchmark (GKB-1000) is an advanced question-answer benchmark designed to evaluate AI models across five areas of general knowledge, reasoning, scientific understanding, communication and reliability.

It contains 1,000 questions, each paired with a reference answer and additional metadata.

GKB-1000 aims to provide a structured evaluation resource for comparing model capabilities, identifying weaknesses and studying performance across different reasoning domains.

Benchmark Objectives

The benchmark is designed to assess:

* General knowledge and factual understanding.
* Logical and mathematical reasoning.
* Scientific and technical knowledge.
* Language comprehension and communication.
* Adaptive and agentic reasoning.
* Critical thinking and reliability.

Dataset Composition

The dataset contains five categories with 200 questions each.

Category	Questions
Logical and Mathematical Reasoning	200
Scientific and Technical Knowledge	200
Language and Communication	200
Agentic and Adaptive Reasoning	200
Critical Thinking and Reliability	200
Total	1,000

1. Logical and Mathematical Reasoning

Evaluates logical deduction, mathematical problem-solving, structured reasoning and analytical thinking.

2. Scientific and Technical Knowledge

Evaluates scientific understanding, technical concepts and the ability to reason about complex subjects.

3. Language and Communication

Evaluates language comprehension, interpretation, explanation and communication.

4. Agentic and Adaptive Reasoning

Evaluates planning, decision-making, adaptation and reasoning in multi-step situations.

5. Critical Thinking and Reliability

Evaluates critical analysis, uncertainty awareness, consistency and the ability to recognize unreliable assumptions.

Dataset Structure

The dataset is provided in JSONL format.

Each line represents one independent JSON record.

Example

{
  "id": "gkb_0001",
  "category": "logical_mathematical_reasoning",
  "difficulty": "Advanced",
  "family": "example_family",
  "question": "Example question",
  "answer": "Example reference answer",
  "skills": ["reasoning", "problem_solving"]
}

This is an illustrative schema example.

Fields

Field	Type	Description
id	String	Unique question identifier
category	String	Benchmark category
difficulty	String	Assigned difficulty
family	String	Question family
question	String	Question presented to the model
answer	String	Reference answer
skills	Array	Relevant skills

Reference Answers

All 1,000 questions include reference answers.

The answer field provides a reference response for each question.

These answers support:

* Answer-based evaluation.
* Supervised fine-tuning.
* Knowledge distillation.
* Model output comparison.
* Error analysis.

For unbiased evaluation, reference answers should not be exposed to the model being tested.

Intended Use

GKB-1000 is intended for:

* AI model benchmarking.
* General knowledge evaluation.
* Reasoning capability assessment.
* Model-to-model comparison.
* Knowledge distillation.
* Supervised training experiments.
* AI research and development.
* Evaluation pipeline testing.

Evaluation Methodology

A recommended evaluation procedure is:

1. Select the model to evaluate.
2. Present the benchmark questions without reference answers.
3. Collect the model’s responses.
4. Compare responses against the reference answers.
5. Assign scores using appropriate evaluation criteria.
6. Calculate overall and category-specific performance.
7. Analyze errors and identify areas of weakness.

Scoring

Different question types may require different scoring methods.

* Exact match: For questions with objectively precise answers.
* Rubric-based scoring: For open-ended explanations and reasoning.
* Semantic comparison: For answers that may use different wording while preserving the same meaning.

Evaluation protocols should be consistent across models.

Benchmark Integrity

To support meaningful evaluation:

* Keep reference answers separate from model inputs.
* Avoid training on the evaluation questions.
* Use consistent prompts and evaluation settings.
* Record model versions and configurations.
* Verify reference-answer accuracy.
* Review ambiguous or potentially duplicated questions.
* Document scoring methods.

Training and evaluating on the same questions can produce misleading performance results.

Limitations

GKB-1000 is an initial benchmark release.

Its publication as a benchmark does not automatically establish standardized validity or scientific reliability.

Further validation should include:

* Independent reference-answer verification.
* Question-quality assessment.
* Difficulty calibration.
* Duplicate and ambiguity detection.
* Evaluation-protocol validation.
* Testing across multiple independent models.

Results should be interpreted with these limitations in mind.

Dataset Format

* Format: JSONL
* Encoding: UTF-8
* Records: 1,000
* Categories: 5
* Reference answers: Included

Version History

Version	Description
GKB-1.0	Initial release containing 1,000 advanced Q&A records across five categories.

License

No license has been assigned yet. A license should be selected and explicitly declared before redistribution or reuse.

Citation

If you use GKB-1000 in research or evaluation, cite the dataset repository and its version.

Suggested citation format:

@misc{gkb1000,
  title = {General Knowledge Benchmark (GKB-1000)},
  version = {1.0},
  year = {2026},
  note = {Advanced question-answer benchmark}
}

Acknowledgements

GKB-1000 is intended to contribute to research and experimentation in AI evaluation, general knowledge assessment and reasoning capabilities.