The Key to LLM Success: Human Testing
As organizations push the limits of AI in their digital products, a key factor in the success of those experiences cannot be understated: humans.
In a recent webinar titled How Human Testing Helps Overcome LLM Limitations, industry experts Chris Sheehan and Josh Poduska discussed the role of human testing in surpassing the limitations of these systems. Organizations must understand and address LLM limitations to succeed in product delivery.
Webinars
How Human Testing Helps Overcome LLM Limitations
Explore the critical role of human validation in LLM development to ensure safe, accurate, and fair AI outputs in our expert-led webinar.
Let’s dig into the key insights from the webinar, highlighting the importance of human involvement in AI development and testing, especially as it continues to evolve.
Emerging Trends in AI
Josh Poduska, client partner and AI strategist at Applause, always has his eye on trends in AI and ML. To kick off the discussion, he highlighted three key areas shaping the AI landscape:
Globalization of AI – AI development has historically been prominent in and led by Western cultures. However, AI is increasingly becoming a global endeavor, which fundamentally changes business and technology strategy. Understanding and preparing for globalized AI is essential to ensure LLMs effectively localize and serve diverse user bases worldwide.
Integrated applications – The era of standalone chatbots is giving way to integrated applications, which means generative AI is becoming a part of everyday tools. Examples like Microsoft’s Copilot illustrate how AI will embed within software suites, enhancing functionality and the user experience.
Multiple modalities – Human interaction with AI is evolving beyond keyboard inputs. Future AI interactions will involve voice, AR/VR, wearables and other modalities. AI systems must be adaptable and versatile to handle these different inputs, capable of functioning across various platforms and interaction methods.
The Importance of Human Testing in AI Development
While AI is flexing some impressive capabilities, systems designed for humans still require human validation to succeed in the marketplace. Chris Sheehan, EVP of high tech and AI sales at Applause, emphasized two primary concerns: bias and safety. By involving humans in the AI development process, organizations can ensure data diversity, enhance model accuracy and mitigate safety risks.
At the end of the day, your goal is to create a great product while at the same time you reduce the many risks that we know LLMs can produce.
– Chris Sheehan
Addressing biases and ensuring data diversity – AI models trained on biased data perpetuate and even exacerbate those biases, which might have unfortunate and even tragic human outcomes. Human testers help identify and correct these biases by providing diverse data inputs and feedback, ensuring fair and representative conclusions from AI models.
Enhancing accuracy and safety – Human involvement in testing ensures that AI models produce accurate and safe outputs. This is particularly important in applications where incorrect or harmful responses can have serious consequences, both for the business and the individual.
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5 Areas Where Human Testing Improves LLM Applications
Chris and Josh used the acronym DET (data, evaluation and testing) to encapsulate how humans can play a critical role in the AI dev pipeline. Here are five key areas where humans make a big difference, separated by the DET categories above.
Training data for AI – High-quality, diverse training data will always be the cornerstone of effective AI models. Human involvement in data labeling and collection helps achieve diverse outputs. For example, to create a globally applicable language model, organizations must gather data from various linguistic and cultural backgrounds.
Fine-tuning LLM data and responses – Fine-tuning involves customizing a general-purpose LLM for specific tasks or domains. Human feedback, particularly through techniques like reinforcement learning with human feedback (RLHF), helps ensure that models’ responses align with human preferences and expectations.
LLM evaluation – Evaluating LLM performance involves defining quality metrics such as accuracy, relevance, coherence and tone. Human evaluators are naturally better at this task than an AI system, as they can manually assess these attributes to ensure the model meets the desired quality standards. LLM evaluation mitigates risks from bias and toxicity.
Red-team testing – This form of adversarial testing, which has been around for a long time as a cybersecurity tactic, identifies potential biases, inaccuracies and safety risks in AI models. Red teaming often involves charting these concerns on a risk matrix or a harm vector to contextualize the severity. Combining generalist feedback with adversarial techniques provides a comprehensive understanding of the model’s vulnerabilities, enabling developers to address them effectively.
Pre-launch testing – Before hitting the deploy button, comprehensive software testing is essential for high quality. Pre-launch QA strategy can include regression testing to ensure new AI features do not disrupt existing functionalities, as well as real user feedback and accessibility testing to further validate the application’s readiness for launch.
Practical Takeaways for Implementing Human Testing
It’s one thing to understand the need for human testing, and it’s another to implement it effectively at a global scale. Our experts provided several practical recommendations for integrating human testing into AI development processes.
Invest in data collection and labeling – Establish dedicated teams for data collection to establish proper diversity and quality standards. Loop in the legal team to weigh in on data privacy and consent concerns.
My main advice here is, be ready to invest in data collection in a way that you hadn’t perhaps thought of before and is quite different than just simply scraping the internet.
– Josh Poduska
Adopt iterative fine-tuning and evaluation – Fine-tuning and evaluation must be continuous, with frequent iterations based on human feedback. This approach helps promote accuracy and relevance in the models over time.
Implement robust red teaming – Develop a comprehensive red-team testing strategy. Combines generalist feedback with adversarial techniques to achieve desirable results. Regularly update this strategy to adapt to emerging risks and regulations.
Ensure comprehensive QA and accessibility testing – Conduct regression testing, collect user experience feedback and promote accessibility testing in your QA plans. A multi-faceted approach is crucial for delivering high-quality, user-friendly AI applications.
For organizations looking to enhance their AI and software testing strategies, Applause offers a comprehensive suite of solutions, including functional and red-team testing, real-world data collection and deep UX research. By leveraging Applause’s expertise, businesses can confidently navigate the complexities of AI development as the technology evolves and deliver exceptional digital experiences.
Let’s chat today about how Applause can support your unique AI initiatives.
10 min