Model Evaluation by LLM-as-a-Service Autorater

Machine learning is evolving rapidly, and a key challenge is how to evaluate and optimize AI models. As AI technologies like natural language processing and reinforcement learning advance, efficient model evaluation becomes more important.

Xinru Yang, a Software Engineer at Google with experience at Microsoft and Alibaba, is at the forefront of these developments! In this interview, she shares her insights into model evaluation, the role of LLM-as-a-service autoraters, and the future of AI technologies. Xinru will also be speaking at the 10th edition of the Data Innovation Summit 2025, where she’ll dive deeper into these topics and more.

Hyperight: Xinru, can you give us a brief overview of your professional background and current working focus?

Xinru Yang: I have been working and conducting research in AI throughout my career, focusing on content understanding from unstructured data and model evaluation. Currently, I am a Software Engineer in Machine Learning at Google, working on multimodal large language models. Previously, I have worked on Search Generative Experience, menu understanding from user-generated content (UGC) photos, and price level prediction using machine learning for local search.

Beyond my industry experience, I actively contribute to academia by reviewing papers for top AI conferences such as CVPR and NeurIPS.

Hyperight: You’ve had experience at top tech companies like Google, Microsoft, and Alibaba. How have those environments shaped your approach to developing AI and machine learning models? Particularly in areas like natural language processing.

Xinru Yang: Each company has offered a unique perspective. At Microsoft Research Asia, I worked on question answering (QA) for large-scale search engines, which built my foundation in NLP. At Alibaba, I focused on recommendation systems, which helped me develop expertise in reinforcement learning-based optimization for commercial AI applications. Now, at Google, I work on evaluating and optimizing search models for improved query intent understanding and personalization. These experiences have reinforced my approach to balancing cutting-edge model performance with practical deployment challenges.

Hyperight: Xinru, in your presentation at the Data Innovation Summit 2025, you will talk about model evaluation. Can you explain what a model development cycle looks like and why model evaluation is important?

Xinru Yang: A typical ML model development cycle consists of data collection, model training, fine-tuning, evaluation, and deployment. Model evaluation is critical because it ensures the model performs well on real-world tasks and user needs. Challenges include creating high-quality labeled data, defining meaningful evaluation metrics, handling domain shifts, and balancing precision-recall trade-offs.

Hyperight: What is an LLM-as-a-service autorater, and how does it help streamline the evaluation process?

Xinru Yang: An LLM-as-a-service autorater is a large language model-driven automated evaluation system used to assess model quality without relying on human raters. It streamlines evaluation by generating human-verified samples, applying few-shot learning techniques, and integrating real-time model monitoring into the training pipeline.

For instance, in my work at Google, I designed an LLM-based auto-evaluation system that reduced evaluation time from weeks to hours and increased iteration speed significantly.

Hyperight: Building an autorater system isn’t simple. Can you walk us through the steps of how to build one? Are there any risks or challenges you need to be mindful of?

Xinru Yang: The key steps include:

1. Data Collection & Annotation: Gather diverse and representative labeled data.
2. Baseline Model Setup: Establish baseline performance metrics and evaluation criteria.
3. LLM Integration: Use few-shot prompting techniques with high-quality reference data.
4. Evaluation Metric Design: Define custom scoring algorithms to assess model quality.
5. System Integration: Embed the autorater into the model training dashboard for real-time tracking.
6. Human Review & Iteration: Validate results against human raters to refine the system.

Challenges:

• Bias in evaluation (models might inherit biases from training data).
• Drift in model performance (evaluation must adapt to new data distributions).
• Scalability issues (large-scale evaluations require computational efficiency).

Hyperight: For those who work on similar projects, what are some best practices to keep in mind when integrating an LLM-based autorater into their workflows?

Xinru Yang:

• Ensure diversity in evaluation data to prevent biased assessments.
• Automate real-time model monitoring to detect performance degradation early.
• Use human raters selectively for validation and error analysis.
• Optimize computational costs by leveraging efficient inference techniques (e.g., LoRA-based fine tuning).
• Continuously update the autorater with new task definitions and emerging use cases.

Hyperight: Given your background in machine learning and natural language processing, what do you think is a major challenge the industry is facing when it comes to scaling LLMs?

Xinru Yang: The biggest challenges include:

• Latency & efficiency: LLMs require significant computational resources, leading to cost and scalability concerns.
• Data privacy & security: Ensuring LLMs do not leak sensitive information while maintaining personalization.
• Evaluation & explainability: Understanding why an LLM produces specific outputs remains a challenge.

For instance, in my work at Google, reducing latency by 40% was a critical breakthrough for the Magi model, improving both quality and efficiency.

Hyperight: Looking ahead, what do you see as the most exciting breakthroughs in machine learning and natural language processing in the upcoming years?

Xinru Yang:

• Multi-modal LLMs: Integration of text, image, and video for richer AI experiences.
• Efficient LLM deployment: Techniques like quantization, distillation, and LoRA finetuning for cost-effective scaling.
• Self-learning AI systems: More autonomous, continually improving models.
• LLM-powered agents: AI assistants capable of executing complex reasoning tasks with minimal human intervention.

Don’t miss Xinru’s presentation at the Data Innovation Summit 2025! She’ll dive into machine learning model evaluation and share her expertise on optimizing AI for real-world applications. She’ll also explore the role of LLM-as-a-service autoraters, and how they can streamline the evaluation process and enhance model performance. If you’re passionate about advancing AI technologies, improving model efficiency, or scaling LLMs, this is a session for you!