Uncover the latest AI trends in Appen's 2024 State of AI Report.

LLM Training Data & Services

With over 25 years of experience, Appen is the leading provider of high-quality LLM training data and services. Whether you're building a foundation model or need a custom enterprise solution, our experts are ready to support your specific AI needs throughout the project lifecycle.

A Powerful Performer

50M+
people hours on the platform in production
20K+
AI projects completed
100M
LLM data elements completed
10B
units of data
In use today by over 80% of leading LLM builders

How to Train an LLM

The LLM lifecycle begins with curating a diverse dataset to equip your model with relevant language and domain expertise. Developing foundation models and training LLMs for multi-modal applications involves processing vast amounts of raw data, including text, images, videos, and audio, to help the model understand human language and various media types effectively.

LLM Fine Tuning

Once your foundation model is built, further training is required to fine tune your LLM. Optimize model performance for specific tasks and use cases by introducing labelled datasets and carefully engineered prompts curated to the target applications.

ebook

Guide to Chain-of-Thought Reasoning

Guide to CoT reasoning for LLMs featuring an expert case study on how Appen built a mathematical reasoning dataset for a leading technology company.

LLM Benchmarking & Evaluation

LLM’s should be evaluated continuously to improve the accuracy of the model and minimize AI hallucinations. Create quality assurance standards for your LLM and leverage human expertise to evaluate your model against those guidelines.

Industry Perspectives

Learn how industry leaders leverage high-quality data to improve their models.

Evaluation of human preferences over model outputs provides critical signals for measuring performance. As part of our development process, we conduct human evaluation extensively across targeted capabilities.
Gemini: A Family of Highly Capable Multimodal Models
December 2023
(Llama 3) does not deviate significantly from Llama and Llama 2 in terms of model architecture.
Our performance gains are primarily driven by improvements in data quality and diversity as well as increased training scale
The Llama 3 Herd of Models
July 2024
Frontier threats red teaming requires investing significant effort to uncover underlying model capabilities.
The most important starting point for us has been working with domain experts with decades of experience.
Frontier Threats Red Teaming for AI Safety
July 2023
As with prior GTP models, we fine-tune the model’s behavior using reinforcement learning with human feedback (RLHF) to produce responses better aligned with the user’s intent
GTP-4 Technical Report
March 2023

LLM Data Solutions

Data quality is the greatest differentiator when it comes to training your large language model. Innovative AI requires high-quality datasets curated to diverse applications. As the leading provider of AI training data, top LLM builders count on Appen to train and evaluate their models across different use cases, languages, and domain expertise.

Supervised Fine Tuning (SFT)

Create custom prompts and responses tailored to diverse data requirements to enhance your model’s performance across different use cases and specialized domains.

Supporting diverse data requirements including:

  • Different use cases: Open QA, Summarization, Rewrite, Chain-of-Thought reasoning, and more.
  • Specialized domains: Subject matter expertise in areas such as math, finance, coding, and healthcare.
  • Multiple languages: 235 + languages including English, Spanish, and Japanese. 

Human-in-the-Loop (HITL)

Leverage Appen’s AI Chat Feedback tool to enhance your model with Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). 

Key Capabilities: 

  • Supports custom workflows and training requirements
  • Single or multi-turn conversations
  • Customizable annotation fields
  • Real-time human interactions

LLM evaluation & A/B testing

Assess the performance of your model across a range of LLM evaluation metrics such as relevance, accuracy, helpfulness, and coherence.

Benefits include: 

  • Targeted insights into strengths and improvement areas
  • A/B testing to compare different models through the development cycle
  • Benchmarking against competitors and other LLMs on the market

LLM red teaming & model safety

Leverage Appen’s red teaming crowd to proactively identify vulnerabilities and ensure the safety and security of your LLM across diverse applications.

Conduct open-ended or targeted red teaming tasks such as:

  • Adversarial attacks
  • Harms categories (toxicity, bias, privacy, etc.)
  • Multi-turn scenario-based testing 
  • Guardrails testing
  • Moderation and annotation of generated content

Retrieval-Augmented Generation 
(RAG)

Tailor your model to specific domains and generate more precise and contextually relevant responses by introducing a broader, external knowledge base.

Retrieval-Augmented Generation (RAG) data services include:

  • Data Preparation: Collect, annotate and curate datasets for your unique use case.
  • Prompt Dataset Creation: Generate effective prompts for effective model training.
  • Evaluation and A/B testing: Compare performance across models and refine outputs.
  • Red Teaming: Stress-test your model to preemptively identify and resolve vulnerabilities.

Kickstart your AI Journey

Our team offers customized solutions to meet your specific AI data needs, providing in-depth support throughout the project lifecycle.

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