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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 model training pipeline. We offer flexible and customizable delivery formats, APIs, and the option for custom API integrations to support rapid development of models.
With over 15 years of industry experience, Sama’s data annotation and validation solutions help you build more accurate GenAI and LLMs—faster.
Our data experts will review your model’s responses for accuracy, identify and highlight any errors, and rewrite responses to improve model performance, combining workflow automation with our human-in-the-loop approach to ensure speed and quality.
Our team can assess how well your Gen AI model understands, interprets, and executes instructions. We’ll help you identify where your model doesn’t comply, including why a response was selected. Any issues are highlighted and flagged, making it easier and more efficient to fine-tune.
Sama’s highly trained team of experts can help you improve the quality and alignment of model outputs through feedback loops, RLHF, and more. With domain expertise across multiple industries and functions, we can analyze and rank model responses, indicate the rationale behind each choice, and highlight any issues within the outputs.
Sama can help you scale captioning for a variety of modalities. Our team of experts will describe the content of visual inputs, verify if the captions match, and rewrite captions as needed to retrain the model to reduce errors and hallucinations. Sama’s proprietary platform makes sampling easy and our collaborative workflows help reduce subjectivity and ambiguity from project kickoff.
With domain expertise across a variety of industries and functions, Sama’s dedicated team can create new prompts and responses based on your model goals. We can also rewrite responses, tailored to model capabilities and limitations, to augment existing training data. Our team can also employ chain of thought to provide clear rationale for chosen outputs.
When real training data is too difficult or not cost effective to obtain, our team can create synthetic data sets to help train your model, using a human-in-the-loop approach to ensure the highest level of quality. Our team will define objectives for your data, including a specific domain or other required parameters, and test outputs for quality and accuracy by comparing them against outputs from authentic data.
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.
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.
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.
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.
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.
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Learn more about Sama's work with data curation
The validation places Sama among more than 5,000 companies and financial institutions across the globe that have made the commitment to supporting climate action by setting science-aligned goals and transparently measuring progress toward achieving them.
Model evaluation in generative AI is the process of assessing how well a model performs its task of creating new data. Unlike traditional machine learning models that predict outputs based on existing data, generative models aim to produce entirely new content, like text, code, or images. Evaluating these models goes beyond simple accuracy and delves into qualities like coherence, creativity, and alignment with the intended use.
Model evaluation solutions help identify weaknesses in areas like factual correctness, coherence, and alignment with the user's intent. Furthermore, it allows us to assess potential biases within the model and mitigate them before they translate into real-world consequences. By continuously evaluating generative AI models, we can ensure they produce valuable, reliable, and ethically sound outputs.
Reinforcement learning from human feedback (RLHF) helps generative AI models learn by rewarding them for creating outputs that align with human preferences. By incorporating human feedback into the training process, we can reward the model for generating outputs that meet desired criteria. This feedback loop allows the model to learn what constitutes good quality content and iteratively improve its performance.
Model hallucinations refer to the phenomenon where a model produces outputs that are factually incorrect, nonsensical, or misleading, despite appearing convincing on the surface. This can happen for several reasons. The training data might be insufficient or biased, leading the model to learn inaccurate patterns. Alternatively, the model might lack the necessary context to understand the nuances of a prompt, resulting in fabricated details or illogical connections.
Model evaluation solutions can help identify bias by analyzing the outputs for fairness across various demographics, social groups or other dimensions. This might involve metrics that measure representation in generated content or flag outputs containing stereotypes.