With Large Language Model innovation happening at a rapid pace, LLMs are presenting both opportunities and challenges for practitioners. One of the most prominent problems they face is how to strategically select the most suitable model for a specific enterprise application – a decision that holds far-reaching consequences for aspects like user experience, maintenance and profitability. Model selection requires a comprehensive evaluation of various factors to navigate this complex landscape, which is now streamlined with the introduction of Appen’s Benchmarking Solution: a groundbreaking tool to reduce risk by selecting the right model.
There are several aspects to consider when evaluating LLMs for enterprise applications. For example:
- Model Size and Capabilities play a pivotal role in performance. Larger models offer enhanced capabilities, while smaller ones might be best suited for more specialized use cases.
- Performance and Customization Options are also equally critical. The capacity to fine-tune models for optimal performance or tailor them to specific needs is pivotal.
- Ethical Considerations loom large, as well. Models designed with safeguards against harmful biases and dangerous outputs help mitigate potentially detrimental business risks.
To illustrate, envision a fashion and home goods retailer aiming to integrate a shopping assistant chatbot into its website. Selecting an appropriate LLM necessitates weighing factors wisely: The chatbot's size and knowledge scope should align with the retailer's domain while fine-tuning capabilities are essential to tailor responses to shoppers' inquiries and keep up with latest trends. Prioritizing safety and ethical design also prevents hallucinations or biased responses that could be brand-damaging.
Appen’s Benchmarking Solution emerges as an invaluable aid in streamlining the model selection process by adding a trust layer into the selection process. We’ve created this tool to evaluate LLMs based on commonly used dimensions like helpfulness, honesty and harmlessness, or fully custom dimensions. Combined with a curated Crowd, it can evaluate model performances along demographic areas of interest such as gender, ethnicity and language. Within the platform, the benchmarking template accelerates project setup and the configurable dashboard enables efficient comparison across models as well as across various dimensions of interest.
Appen’s Benchmarking Solution manages the complex task of quality assurance with transparency and meticulous analysis. Our platform allows us to monitor performance on the basis of individual contributors, helping find and retain top talent, as well as includes dashboards that give our customers full visibility into the process. With trained specialists in the loop, enterprise models reflect the fluency, creativity and guidelines of the brand. And as part of the white-glove service, our project-dedicated staff attend to the nuances of your data and model, analyzing each bath of data delivered, surfacing edge cases and reducing risks that result from loose or non-bespoke monitoring.