Project Overview
As part of AI model development projects commissioned through Scale AI and Outlier AI, I contributed to the training, evaluation, and linguistic refinement of multilingual large language models (LLMs). My work combined advanced language expertise, editorial judgment, fact-checking, prompt design, and structured evaluation of AI-generated content.
Working across multilingual datasets, including Arabic, French and English, I assessed whether AI-generated responses were not only grammatically correct but also natural, coherent, contextually appropriate, culturally relevant, factually reliable, and aligned with user intent.
Key Contributions
Multilingual linguistic annotation and editing
- Reviewed and refined AI-generated content for orthography, grammar, morphology, semantics, vocabulary, punctuation, syntax, and stylistic consistency.
- Identified subtle language problems that can affect model quality, including incorrect article and pronoun usage, subject–verb and gender/number agreement, inappropriate prepositions and conjunctions, conjugation errors, spelling inconsistencies, and unnatural phrasing.
- Edited machine-generated responses to achieve the level of fluency and naturalness expected from a proficient native-language speaker.
- Evaluated linguistic and cultural appropriateness rather than relying solely on literal grammatical correctness.
Prompt design and multilingual model evaluation
- Created original, nuanced prompts designed to test an AI model’s ability to understand context, instructions, constraints, tone, language, and user intent.
- Developed prompts across different complexity levels and use cases, including rewriting, summarization, question answering, advice, explanatory content, and creative or analytical tasks.
- Evaluated competing AI responses against structured criteria, identifying differences in instruction following, factuality, fluency, coherence, tone, presentation, concision, localization, and safety.
- Conducted side-by-side preference ranking and provided evidence-based justifications explaining why one response performed better than another.
Fact-checking and response quality assurance
- Fact-checked factual claims and identified hallucinations, misleading statements, incomplete answers, and contextual inaccuracies.
- Distinguished between instruction-following failures and factual errors, ensuring that each issue was classified according to the appropriate evaluation dimension.
- Verified information through reliable external sources when required and assessed whether responses were appropriately grounded in reference material.
- Rewrote and corrected selected responses to produce high-quality benchmark outputs suitable for model improvement.
AI training data curation
- Curated and organized multilingual training data to improve the relevance, consistency, diversity, and quality of datasets used for model evaluation and fine-tuning.
- Supported data labeling and classification processes, ensuring that content was accurately categorized for downstream model assessment.
- Applied detailed project-specific rubrics and quality standards consistently across large volumes of language data.
- Contributed human linguistic judgment to areas where automated evaluation cannot reliably distinguish between technically correct language and genuinely natural communication.
Evaluation Framework
My work required assessing AI responses across multiple dimensions, including:
- Instruction Following: whether the model understood and satisfied the complete prompt and its constraints.
- Truthfulness / Factuality: whether claims were accurate, verifiable, and free from hallucinations.
- Language Fluency: grammar, spelling, syntax, vocabulary, naturalness, and linguistic precision.
- Coherence: logical organization, clarity, consistency, and continuity of ideas.
- Localization: cultural relevance and authenticity for the target language and regional context.
- Tone & Style: appropriateness, warmth, professionalism, and adherence to requested communication style.
- Conciseness: ability to communicate the necessary information without unnecessary verbosity.
- Safety / Harmlessness: identification of potentially harmful, offensive, misleading, or privacy-sensitive content.
Impact
This work strengthened my ability to operate at the intersection of language, communication, data quality, and artificial intelligence. It required translating nuanced human judgments about language and meaning into structured evaluation criteria that could be consistently applied to AI-generated content.
The experience provided hands-on exposure to LLM evaluation, RLHF-style preference assessment, multilingual data annotation, prompt engineering, response optimization, fact-checking, and human-in-the-loop AI development, with particular expertise in evaluating the quality and naturalness of Arabic and French content.