GENERATIVE AI

Model Evaluation For Gen AI & LLMs

Boost model accuracy and performance, responsibly. Our diverse team of data experts puts humans in the loop early in the process to improve generative outputs.

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25% of Fortune 50 companies trust Sama to help them deliver industry-leading ML models

SOLUTIONS

Model Evaluation Solutions 

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Prompt Classification & Augmentation

Assessing user queries involves checking the clarity of the user's question for accuracy, helpfulness, and adherence to policies. Each aspect is assigned a rating using predefined codes for prompt classification. Our team also improves prompts by rewriting for clarity and intent.

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Conversational AI

Our team helps build smarter conversational AI solutions by creating annotated datasets that help train LLMs to master the nuances of the English language. This highly nuanced data enables models to deliver natural and engaging responses within conversational AI systems.

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Model Output Evaluation
(RLHF, DPO)

Our team will validate data created by generative models—including text, images and videos—for alignment. We’ll review images or other synthetically generated data to ensure they are real-world scenarios. Logical errors are identified and re-annotated to create additional training data sets to fine tune generative models.

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Factual Checks To Reduce Hallucinations

Sama’s team of experts review replies to ensure they are factually correct and do not contain any leaps in logic. Any errors  identified are re-created to develop additional training data to fine tune and align generative models.

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Multimodal Consistency

Sama's workforce reviews Generative AI outcomes, analyzing images in context, classifying production modalities, and tagging elements. This human validation enhances the accuracy and relevance of the annotations, ensuring they align with specific use cases.

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Question-Answering Systems

Our team can help create annotated datasets to help LLMs grasp the nuances of language, understand relationships between concepts, and identify key information within text to comprehend complex questions. 
This helps models understand and respond to user queries more accurately.

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Bias Detection

Sama's workforce reviews responses and flags any statements that reinforce stereotypes or make unwarranted generalizations. This helps improve overall representation and inclusivity.

APPROACH

Our Proprietary Approach to Data Creation

Sama’s model evaluation projects start with tailored consultations to understand your requirements for model performance. We’ll align on how you want your model to behave and set targets across a variety of dimensions.

Our team of Solutions engineers will collaborate with your team to connect to our platform and ensure a smooth flow of data. This can involve either connecting to your existing APIs or having custom integrations built specifically for your needs.

Our expert team meticulously crafts a plan to systematically test and evaluate model outputs to expose inaccuracies. We follow a robust evaluation process that involves a thorough examination of both prompts and the corresponding responses generated by the model. We will assess these elements based on predefined criteria, which may include factors like factual accuracy, coherence, consistency with the prompt's intent, and adherence to ethical guidelines. 

As errors in model outputs are identified, our team will begin creating an additional training data set that can be used to finetune model performance. This new data consists of rewritten prompts and corresponding responses that address the specific mistakes made by the model.

When the project is complete, we follow a structured delivery process to ensure smooth integration with your LLM training pipeline. We offer flexible and customizable delivery formats, APIs, and the option for custom API integrations to support rapid development of models.

SAMA GEN AI

Generative AI and LLM Solutions

With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI and LLMs—faster.

Model Evaluation

Our human-in-the-loop approach drives data-rich model improvements & RAG embedding enhancements through a variety of validation solutions. Our team provides iterative human feedback loops that score and rank prompts along with evaluating outputs. We also provide multi-modal captioning and sentiment analysis solutions to help models develop a nuanced understanding of user emotion and feedback.

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Training Data

We’ll help create new data sets that can be used to train or fine tune models to augment performance. If your model struggles with areas such as open Q&A, summarization or knowledge research, our team will help create unique, logical examples that can be used to train your model. We can also validate and reannotate poor model responses to create additional datasets for training.

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Supervised Fine-Tuning

Our team will help you build upon an existing LLM to create a proprietary model tailored to your specific needs. We’ll craft new prompts and responses, evaluate model outputs, and rewrite responses to improve accuracy and context optimization.

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Red Teaming

Our team of highly trained of ML engineers and applied data scientists crafts prompts designed to trick or exploit your model’s weaknesses. They also help expose vulnerabilities, including generating biased content, spreading misinformation, producing harmful outputs and more to improve the safety and reliability of your Gen AI models. This includes large scale testing, fairness evaluation, privacy assessments and compliance.

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PLATFORM

What Our Platform Offers

Multimodal Support

Our team is trained to provide comprehensive support across various modalities including text, image, and voice search applications. We help improve model accuracy and performance through a variety of solutions.

Proactive Quality at-Scale

Our proactive approach minimizes delays while maintaining quality to help teams and models hit their milestones. All of our solutions are backed by SamaAssure™, the industry’s highest quality guarantee for Generative AI. 

Proactive Insights

SamaIQ™ combines the expertise of the industry’s best specialists with deep industry knowledge and proprietary algorithms to deliver faster insights and reduce the likelihood of unwanted biases and other privacy or compliance vulnerabilities.

Collaborative Project Space

SamaHub™, our collaborative project space, is designed for enhanced communication. GenAI and LLM clients have access to collaboration workflows, self-service sampling and complete reporting to track their project’s progress.

Easy Integrations

We offer a variety of integration options, including APIs, CLIs, and webhooks that allow you to seamlessly connect our platform to your existing workflows. The Sama API is a powerful tool that allows you to programmatically query the status of projects, post new tasks to be done, receive results automatically, and more.

99%

First batch client acceptance rate across 10B points per month

3X

Get models to market 3x faster by eliminating delays, missed deadlines and excessive rework

65K+

Lives impacted to date thanks to our purpose-driven business model

RESOURCES

Popular Resources

Learn more about Sama's work with data curation

Supervised Fine-Tuning: How to Choose the Right LLM
BLOG
7
MIN READ

Supervised Fine-Tuning: How to Choose the Right LLM

Large language models (LLMs) have emerged as powerful tools capable of generating human-like text, understanding complex queries, and performing a wide range of language-related tasks. Creating them from scratch however, can be costly and time consuming. Supervised fine-tuning has emerged as a way to take existing LLMs and hone them to a specific task or domain faster.

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BLOG
5
MIN READ

Faces of Sama: Erick Vukaya’s Journey from an Untrained Teacher to a Portfolio Lead

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BLOG
2
MIN READ

Introducing Sama Red Team: Boosting Safety and Reliability for Generative AI Models

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PODCAST
29
MIN LISTEN

StoneX Group Director of Data Science Elettra Damaggio

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Frequently Asked Questions

What is model evaluation in Generative AI?

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Why are model evaluation solutions important in Generative AI?

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Why is RLHF important in Generative AI?

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What are model hallucinations in Generative AI?

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How can model evaluation solutions help you avoid biases in Generative AI models?

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