ACT-GP White Paper: Keyword-Prompt AI Model (Multilingual)

White Paper: Developing a Keyword-Prompt AI Model from Public Repositories and Text Sources (Multilingual)

Executive Summary

The AI Code and Text Generation Platform (ACT-GP) is designed to translate keyword prompts into actionable outputs, such as code snippets, function templates, tutorials, and explanatory text, using public repositories and multilingual text sources. By incorporating structured data management, tokenization, multilingual support, and transparent provenance, ACT-GP mitigates risks associated with opaque models while delivering high-quality outputs.

This white paper provides a comprehensive overview of the end-to-end process, including the nine critical steps: acquisition, cleaning, structuring, tokenization, model selection, training, evaluation, deployment, governance, and internationalization features for multilingual corpora.

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1. Data Acquisition

Sources include:

• Code Repositories: GitHub projects in multiple languages
• Text Sources: Blogspot blogs, public forums, documentation in non-English languages
• Metadata: Author, license, URL, language, timestamp

Language Detection Example

from langdetect import detect

language = detect(text_sample)

Checkpoint

Create a manifest documenting all sources including language and licensing information.

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2. Data Cleaning & Preprocessing

• Text normalization (UTF-8, lowercase, Unicode NFC/NFKC)
• Code formatting: normalize indentation, remove binaries
• Segmentation: functions/classes for code, paragraphs/sentences for text
• Deduplication: hashing, SimHash, MinHash; semantic similarity for multilingual duplicates
• Noise removal: ads, navigation, spam

Checkpoint Metrics

• UTF-8 compliance >99%
• Deduplication ≥95%
• Manual review confirms quality

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3. Data Structuring & Metadata Labeling

• Add metadata: language, source_type, license, topic, author, timestamp
• Optional: regional variants (e.g., pt-BR vs pt-PT)
• Content tagging: code functionality and text type
• Language-aware classification using embeddings or keyword search

Checkpoint Metrics

• Metadata completeness ≥95%
• License coverage ≥80%
• Tag accuracy verified

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4. Tokenization & Encoding

• Tokenizer selection: BPE, SentencePiece, or code-aware tokenizer
• Multilingual support: separate vocabularies for code vs text, cross-lingual embeddings
• Preserve code structure: <FUNC>, <CLASS>
• Storage: Arrow, Parquet, TFRecord

Checkpoint Metrics

• Coverage of all languages in vocabulary ≥95%
• Unknown token rate per language <2%
• Tokenized data successfully loaded

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5. Model Selection & Architecture

• Multilingual transformer or adapter approach
• Code generation: StarCoder, CodeT5, CodeGen
• Text generation: mT5, BLOOM, LLaMA-family
• Hybrid retrieval-augmented generation for multilingual snippets

Checkpoint

Pilot training confirms memory feasibility and throughput.

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6. Training & Fine-Tuning

• Base model training on multilingual corpora
• Fine-tuning: keyword → output pairs per language
• Instruction tuning with multilingual prompts
• RLHF for alignment across languages

Checkpoint Metrics

• Validation loss decreasing per language
• Generated code compiles, even with non-English comments
• Human evaluation confirms quality in multiple languages

Example Multilingual Prompt-Response

Prompt (Spanish): "petición HTTP asincrónica Python"
Output:
def obtener_url(url):
    import aiohttp, asyncio
    async def main():
        async with aiohttp.ClientSession() as session:
            async with session.get(url) as resp:
                return await resp.text()
    return asyncio.run(main())

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7. Evaluation & Validation

• Metrics per language: BLEU, CodeBLEU, exact match
• Compile & execution tests for code snippets
• Human review: native speakers for quality checks
• Bias and safety checks for culturally sensitive content

Checkpoint Metrics

• ≥90% code snippets compile
• ≥80% text outputs high quality per language
• Bias review completed for all major languages

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8. Deployment & Integration

• Language detection at inference to select correct model/adapter
• APIs support UTF-8 and right-to-left scripts
• Localized front-end and IDE plugin UIs
• Feedback loop for retraining and multilingual improvements

Checkpoint Metrics

• API latency <500ms for typical multilingual prompts
• Plugin/website functional with multilingual support
• Feedback loop captures usage per language

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9. Governance, Security & Continuous Learning

• License compliance: verify across international sources
• Privacy protection: multilingual PII detection
• Continuous retraining: ingest new multilingual repos/blogs
• Transparency: model/dataset cards document language support
• Auditing & monitoring: outputs per language, bias, drift

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Ethical & Strategic Considerations

• Intellectual property arms race: multilingual patterns may influence global derivative models
• Tournaments/"Torneo": international coding competitions amplify usage
• Transparency vs competitiveness: balancing compliance with innovation

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Conclusion

The ACT-GP platform provides a systematic approach to building a keyword-driven, multilingual AI generation system. Through structured acquisition, cleaning, multilingual structuring, tokenization, model selection, training, evaluation, deployment, and governance, it ensures scalability, usability, and ethical compliance.

Multilingual support expands the platform’s reach, enabling code and text generation across diverse languages and regions while maintaining quality, safety, and licensing compliance.