Kevin L.

Key Skills

Software

Don't disclose

Top Subject Matter

LLM evaluation and dataset quality assurance

Text classification and sentiment labeling

Entity tagging and structured information extraction

Top Data Types

Text

Top Task Types

Classification

Entity (NER) Classification

RLHF

Freelancer Overview

AI response evaluation and dataset quality control. Core strengths include Don't disclose. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

RLHF preference ranking and feedback tasks

Don't discloseTextRLHF

You conducted RLHF-style preference ranking and feedback tasks by comparing model outputs against quality guidelines. You provided structured comparative evaluations to support preference-based learning. You ensured outputs met task criteria and instruction requirements during ranking. • Preference ranking of model responses • Quality-guideline comparative judgment • Feedback-style evaluation • Instruction compliance checks

Present

Entity tagging for structured extraction

Don't discloseTextEntity (NER) Classification

You performed entity tagging as part of structured information extraction workflows. You applied strict instruction-following to maintain consistent span/entity assignments. You supported downstream machine learning training by providing reliable labeled outputs. • Entity tagging/NER labeling • Structured information extraction • Guideline-driven consistency • Training data readiness support

Present

Structured text annotation (classification and sentiment)

Don't discloseTextClassification

You completed structured annotation tasks including text classification and sentiment labeling. You followed annotation guidelines to ensure consistent labeling across examples. You performed dataset organization and structured data cleanup to maintain annotation quality. • Text classification labeling • Sentiment labeling • Consistency with annotation guidelines • Dataset cleanup and organization

Present

AI response evaluation and dataset quality control

Don't discloseText

You evaluated AI-generated responses for factual accuracy, clarity, and alignment with provided instructions. You applied quality guidelines to check relevance and identify potential hallucinations. You supported iterative dataset improvements based on evaluation outcomes. • Accuracy assessment • Relevance and instruction-alignment checks • Hallucination detection • Output clarity review

Present