How Uber’s Scaled Solutions tests and evaluates LLM and AI models
LLMs (large language models) have become a key focus in the tech world, revolutionizing industries like healthcare, finance, and entertainment. As promising as these models are, however, they come with unique challenges that need rigorous T&E (testing and evaluation) to ensure their safe and effective use. Uber’s Scaled Solutions offers robust testing and evaluation services designed to help companies confidently deploy their AI and LLM systems.
Why testing and evaluation matter for LLMs and AI models
The rise of LLMs has unlocked incredible possibilities, from automating tasks to enhancing decision-making processes. Like any powerful tool, though, these models must be thoroughly tested to mitigate potential risks, including bias, factual inaccuracies, and harmful behaviors. Uber's Scaled Solutions focuses on these risks by implementing structured testing protocols that make sure the models work accurately and responsibly across various industries.
Key areas of focus in LLM evaluation
LLMs require a multifaceted approach when it comes to evaluating their performance. These axes of evaluation help us understand a model's overall capability and its “safety” to use in real-world applications. We break this down into 5 key areas:
- Instruction-following
How well does the model understand and follow the instructions it’s given? This is critical for applications like customer service chatbots or AI assistants.
- Creativity
In scenarios where creativity is needed, such as content generation, we test the model ’s ability to deliver engaging and innovative responses while remaining relevant.
- Responsibility
This area focuses on whether the model avoids generating harmful content, including biases, toxicity, and misinformation.
- 논리적 추론
We evaluate the model’s ability to process complex information and provide sound, logical outputs.
- Factual accuracy
Factuality is essential for AI systems providing information. Our tests ensure that models generate truthful and accurate content.
How Uber helps enterprises with AI model testing
Uber’s Scaled Solutions provides tailored services to help enterprises safely integrate LLMs and AI models into their operations.
We do this through customizable platforms and expert-led evaluations. Our platforms, like uLabel and uTask, ensure scalable workflows that allow businesses to track performance, ensure compliance, and maintain the highest quality across their AI systems.
This flexibility makes sure companies in sectors like healthcare, finance, and automotive can deploy AI models that are not only effective but also safe and reliable.
With these platforms, enterprises can:
Easily manage and orchestrate tasks
Monitor key performance metrics
Use real-time analytics dashboards to track progress
Optimize workflows and feedback loops
Uber’s testing & evaluation regime: comprehensive and ongoing
Our approach to testing and evaluating LLMs isn’t a one-time effort. We adopt continuous testing models that involve human experts and automated processes. Here’s how Uber Scaled Solutions structures its T&E process:
- 1. Model evaluation
This involves periodic evaluations by human experts and automated systems. The goal is to regularly check a model's performance across the 5 areas mentioned earlier. Key activities include:
- Version control and regression testing: We compare different model versions to track improvements or identify any regressions
- Exploratory evaluation: At major development milestones, we conduct in-depth evaluations of the model’s strengths and weaknesses, culminating in a comprehensive report
- 2. Continuous model monitoring
Even after deployment, continuous monitoring ensures that AI models remain aligned with performance expectations. Our automated systems flag any problematic outputs, which are then reviewed by human experts to correct issues and update training datasets.
- 3. Red teaming for safety and security
In this stage, Uber employs a team of human experts who specifically attempt to expose the model's vulnerabilities. This process is designed to catch any harmful behaviors, such as spreading misinformation or generating inappropriate content. Once identified, these issues are cataloged and addressed through further model training and fine-tuning.
결론
Uber’s Scaled Solutions is at the forefront of AI model evaluation, offering comprehensive testing and monitoring to ensure that LLMs and AI models meet the highest industry standards. From reducing risks to enhancing overall performance, our solutions empower businesses to scale their AI systems confidently and effectively.
By partnering with Uber, companies can leverage a tested, structured approach to AI and LLM deployment so that their models remain safe, efficient, and cutting-edge.
Uber Scaled Solutions
With over 8 years of expertise in managing large-scale data labeling operations, we offer 30+ advanced capabilities, including image and video annotation, text labeling, 3D point cloud processing, semantic segmentation, intent tagging, sentiment detection, document transcription, synthetic data generation, object tracking, and LiDAR annotation.
Our multilingual support spans 100+ languages, covering European, Asian, Middle Eastern, and Latin American dialects, ensuring comprehensive AI model training for diverse global applications.
Our solutions include:
Data annotation and labeling: Expert, precise annotation services for text, audio, images, video, and many more technologies
Product testing: Efficient product testing with flexible SLAs, diverse frameworks, 3,000+ test devices, all streamlined for an accelerated release cycle
Language and localization: World-class user experience for everyone, everywhere
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