Alligner
Evaluating “Text-to-Speech” voices for audiobook narration.
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I am an experienced data annotator with a background in evaluating and refining Large Language Models (LLMs) to improve model accuracy, reasoning, and safety. My work includes authoring complex, multi-turn prompts to rigorously test model boundaries, performing detailed fact verification to eliminate hallucinations, and conducting high-volume data labeling tasks focused on intent, linguistic nuance, and safety violations. With a keen eye for detail and strong analytical skills developed through my architectural and research experience, I excel at accurately tagging datasets and providing precise feedback for supervised machine learning. I am proficient in tools such as Revit, AutoCAD, and various design platforms, and I thrive in collaborative, multidisciplinary environments where quality and innovation are paramount.
Evaluating “Text-to-Speech” voices for audiobook narration.
Labelling actions performed by robotic arms in videos, creating accurate clips and descriptions of actions performed.
Evaluating 2 generated images against a given prompt and deciding which one performs better. Searching for images online and labelling objects within the image.
Projects involving a mix of fact-checking, prompt writing and response evaluation, rubrics writing, checking sensitive responses against safety guidelines. Tasks varied from comparison tasks, rating two responses against a set of criteria (truthfulness, tone, formatting); Prompt and response engineering, writing prompts related to a given topic, evaluating 2 responses, writing rubrics to generate a perfect response and writing the ideal response; Creative writing and editing, drafting ideal responses and editing model responses, Fact verification, checking that models are not hallucinating.
Postgraduate Diploma in Architectural Practice and Management, Architectural Practice and Management
Master of Architecture, Architecture
Associate Architect
Architect