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Shan Jiang

Shan Jiang

LLM Evaluation and Text Generation Specialist in English&Chinese

Thailand flagBangkok, Thailand
$25.00/hrIntermediateAppenClickworkerCrowdsource

Key Skills

Software

AppenAppen
ClickworkerClickworker
CrowdSourceCrowdSource
LabelboxLabelbox
OneFormaOneForma
RemotasksRemotasks
Scale AIScale AI

Top Subject Matter

Imagery annotation and evaluation
LLM Evaluation in English, Chinese and Cantonese
Logistics/Business/Social Media

Top Data Types

DocumentDocument
ImageImage
TextText

Top Task Types

Action Recognition
Evaluation Rating
Segmentation
Text Generation
Translation Localization

Freelancer Overview

An adept in text analysis, I've contributed to NLP projects for global clients. With comprehensive linguistic knowledge and LLM training experience, my detail-oriented, logical, and efficient approach ensures precise outcomes. My expertise spans across various tools, setting me apart from others."

IntermediateEnglishCantoneseChinese Mandarin

Labeling Experience

CrowdSource

LLM training

CrowdsourceTextText Generation
The scope of the project involves training a large language model (LLM) over a period of 3-6 months. Each training cycle consists of handling a pool of 2000-6000 tasks. The project's success is contingent upon three key measures: Feasibility Assessment of Prompts: Ensuring that the prompts provided to the model are feasible and conducive to generating accurate and relevant responses. This involves carefully crafting prompts that effectively guide the model towards producing desired outputs within the specified context. Data Analysis in Generated Response: Analyzing the responses generated by the LLM to assess their coherence, relevance, and overall quality. This involves conducting thorough evaluations of the generated text to identify any inconsistencies, errors, or deviations from the intended prompt. Correctness and Factual Accuracy: Verifying the correctness and factual accuracy of the generated responses against the input prompts and reference sources. This includes fact-checki

The scope of the project involves training a large language model (LLM) over a period of 3-6 months. Each training cycle consists of handling a pool of 2000-6000 tasks. The project's success is contingent upon three key measures: Feasibility Assessment of Prompts: Ensuring that the prompts provided to the model are feasible and conducive to generating accurate and relevant responses. This involves carefully crafting prompts that effectively guide the model towards producing desired outputs within the specified context. Data Analysis in Generated Response: Analyzing the responses generated by the LLM to assess their coherence, relevance, and overall quality. This involves conducting thorough evaluations of the generated text to identify any inconsistencies, errors, or deviations from the intended prompt. Correctness and Factual Accuracy: Verifying the correctness and factual accuracy of the generated responses against the input prompts and reference sources. This includes fact-checki

2022 - 2023

Education

H

Hubei Technical Institution

College, Interior Design

College
1998 - 2001

Work History

M

Mango Language

Online Tutor

Bangkok
2023 - Present
F

Freelance Job

Freelancer

Bangkok
2022 - Present