LLM Evaluation: Assess and Improve LLM Performance
Evaluation is essential to improve model performance. Appen’s expert solutions combine human intelligence with powerful LLM evaluation tools to augment LLM training data strategies and capture the qualitative insights that automated metrics often overlook.
How to Evaluate LLMs
LLM evaluation is the process of testing and validating large language models for performance, bias, robustness, and alignment. Leverage a combination of LLM evaluation metrics, benchmarks, and human-in-the-loop (HITL) methods to ensure outputs are ethical, accurate, and aligned with user intent. Incorporating human judgment alongside automated assessments reveals critical issues that standard metrics alone can’t detect.
LLM Evaluation Frameworks
Effective LLM evaluation frameworks combine automated metrics, human judgment, and domain-specific testing to assess a model’s real-world readiness. These frameworks typically include:
General evaluation to assess model performance on different tasks and uses cases
A/B testing for comparative performance throughout the model development lifecycle
Domain-specific assessments tailored to legal, medical, or creative applications
Diverse user demographic testing to evaluate AI safety risks
Comparing performance against other leading models on SOTA benchmarks
Red teaming to identify vulnerabilities or conduct scenario-based testing
A strong framework ensures your evaluation process is repeatable, scalable, and aligned with business goals.
Common LLM Evaluation Metrics
Evaluating LLM performance requires a blend of quantitative and qualitative metrics. These criteria vary dependent across industries and use cases, but often include:
Accuracy & Relevance
Does the output address the prompt correctly and completely?
Factuality:
Are claims verifiable and supported by external knowledge?
Toxicity & Bias
Is the output free from harmful language or stereotypes?
Fluency & Coherence
Is the language grammatically correct and logically structured?
Helpfulness & Alignment
Does the model follow instructions and meet user intent?
Latency & Throughput
How fast and efficiently does the model respond?
These metrics help you compare models objectively while surfacing qualitative concerns that matter in deployment.
Why is LLM Evaluation & Testing Important?
As LLMs are deployed in sensitive and high-stakes domains, robust evaluation frameworks are essential to mitigate risk and ensure trust. Relying solely on automated systems can overlook subtle failures, making human evaluation a key pillar of responsible deployment.
Human Evaluation is Risk Management
Without human oversight, LLMs are more likely to generate misleading, biased, or harmful outputs. Human evaluators act as judges to uncover:
Contextual Failures
LLMs may misinterpret nuanced prompts, especially in multi-domain or creative contexts.
Bias & Unethical Risks
Without human-led testing, models may reinforce stereotypes or output unsafe content.
False Confidence
LLMs often sound fluent even when they’re wrong—only human review can catch this.
Compliance Failures
Ensure alignment with regional laws and regulations through human oversight.
How Appen Supports LLM Evaluation
Appen provides end-to-end evaluation solutions to improve and track your LLM’s performance, as well as compare leading models – like DeepSeek, GPT, and Claude – to find the right fit for your needs.
Benchmarking Datasets
Customised datasets with challenging prompts to test model accuracy and identify improvement areas.
Human-as-a-Judge Evaluation
Human expertise is crucial for safe and reliable model performance across nuanced applications – including LLM agent evaluation.
Ongoing A/B Testing
Compare and validate model performance with consistent, real-world testing iterations.
Cost-Based Model Selection Strategy
Our experts help you choose the right LLMs that balance cost and performance for your specific use cases.
Qualitative Contributor Insights
In-depth human insights to analyse trends and refine performance over time.
AI Data Platform (ADAP)
Our AI Data Platform is a leading tool for efficient, high-quality, and guideline-compliant LLM evaluation.
Why Choose Appen for LLM Evaluation?
Appen combines human expertise, global coverage, and powerful tools like ADAP to deliver comprehensive LLM evaluation frameworks. We support your AI lifecycle with:
Accuracy and Precision
Improve performance in specialised domains, like healthcare and law, with rigorous fact-checking.
Bias and Fairness
Ensure unbiased outputs with robust assessments of decision-making integrity.
Ethical Compliance
Identify and mitigate harmful behaviours to align models with societal norms and regulations.
Latency and Performance
Optimise response time and efficiency to ensure scalability for real-time applications under demanding conditions.
Robustness
Ensure consistent performance by validating your model against ambiguous inputs, edge cases, and stress conditions.
Response Diversity
Enhance contextual adaptability to meet diverse use cases such as education or creative tasks.
Usability
Deliver intuitive, satisfying user experiences by evaluating fluency, coherence, and relevance across diverse scenarios.
Appen in Action
As the leading provider of human evaluation data, Appen has supported top model builders and enterprises in selecting, refining, and testing their models. Our proven expertise in AI evaluation includes impactful projects such as:
Rapid-Sprint LLM Evaluation & A/B Testing in Multiple Domains
Appen partnered with a model builder to conduct rapid-sprint evaluations across 3-6 LLMs for tasks spanning general and complex domains like healthcare, legal, and finance. Using Appen’s team of evaluators and ADAP’s data management tools, we delivered over 500,000 annotations to benchmark accuracy, relevance, and Responsible AI standards.
Training a Graphic Design LLM Image Generator in 20+ Languages
Appen partnered with a leading graphic design software company to enhance their AI model's ability to generate culturally relevant images from text prompts in over 20 languages. By localizing prompts and evaluating design outputs, Appen ensured high-quality, culturally appropriate graphics that met diverse user expectations.
A/B Testing for Legal Domain LLM
Appen improved a legal domain-specific LLM’s performance through precise A/B testing. By leveraging a diverse network of contributors, including legal professionals, we delivered rigorous evaluations with high-confidence insights. The client used these insights to refine their model for cost-efficient and accurate legal applications.
RLHF for Leading Foundation Model Provider
Appen partnered with a top AI provider to enhance response quality using RLHF (Reinforcement Learning from Human Feedback). We trained 50+ contributors to evaluate and rank over 700,000 model-generated responses across diverse domains. This effort refined the client’s reward model, ensured high-quality data, and reinforced their leadership in the AI space.
Next-Generation Benchmarking with Human-AI Co-Annotation
A leading model builder collaborated with Appen to develop an advanced, multi-domain LLM evaluation benchmarks. Appen sourced 40 expert contributors to create over 100 expert-level question sets covering 90+ topics. By employing tools like Model Mate for rationale creation and rigorous QA processes, the project set new standards for benchmarking quality and supported future domain expansions.
Improve LLM Performance Today
Refine your LLMs today with Appen’s expert evaluation and testing. Build ethical, reliable AI solutions tailored to complex real-world challenges.
Scoping Your Project
We start with a flexible proof of concept (PoC) to validate assumptions, assess feasibility, and refine the approach with minimal investment. As results prove successful, we scale across models, languages, and markets. Key factors like number of models, languages, passes, and prompts inform our cost estimate.