Importance of Benchmarks for AI Models

𝔼𝕍𝔸𝕃 field note (2 of 3): Finding the benchmarks that matter for your own use cases is one of the biggest contributors to AI success. Let's dive in. AI adoption hinges on two foundational pillars: quality and trust. Like the dual nature of a superhero, quality and trust play distinct but interconnected roles in ensuring the success of AI systems. This duality underscores the importance of rigorous evaluation. Benchmarks, whether automated or human-centric, are the tools that allow us to measure and enhance quality while systematically building trust. By identifying the benchmarks that matter for your specific use case, you can ensure your AI system not only performs at its peak but also inspires confidence in its users. 🦸♂️ Quality is the superpower—think Superman—able to deliver remarkable feats like reasoning and understanding across modalities to deliver innovative capabilities. Evaluating quality involves tools like controllability frameworks to ensure predictable behavior, performance metrics to set clear expectations, and methods like automated benchmarks and human evaluations to measure capabilities. Techniques such as red-teaming further stress-test the system to identify blind spots. 👓 But trust is the alter ego—Clark Kent—the steady, dependable force that puts the superpower into the right place at the right time, and ensures these powers are used wisely and responsibly. Building trust requires measures that ensure systems are helpful (meeting user needs), harmless (avoiding unintended harm), and fair (mitigating bias). Transparency through explainability and robust verification processes further solidifies user confidence by revealing where a system excels—and where it isn’t ready yet. For AI systems, one cannot thrive without the other. A system with exceptional quality but no trust risks indifference or rejection - a collective "shrug" from your users. Conversely, all the trust in the world without quality reduces the potential to deliver real value. To ensure success, prioritize benchmarks that align with your use case, continuously measure both quality and trust, and adapt your evaluation as your system evolves. You can get started today: map use case requirements to benchmark types, identify critical metrics (accuracy, latency, bias), set minimum performance thresholds (aka: exit criteria), and choose complementary benchmarks (for better coverage of failure modes, and to avoid over-fitting to a single number). By doing so, you can build AI systems that not only perform but also earn the trust of their users—unlocking long-term value.

Large language models like ChatGPT are very good at convincing users their outputs are correct, when they make mistakes all the time. Just this week an education leader in Alaska is in the news because she used GenAI to create a new cellphone policy that included academic studies that did not exist. To help combat these inaccuracies (called hallucinations), OpenAI just introduced their new open-source benchmark, SimpleQA. By focusing on short, fact-seeking queries, SimpleQA’s goal is to assess the “factuality” of language models and help drive “research on more trustworthy and reliable AI.” The benchmark: - Double-verifies answers by independent AI trainers - Includes questions that span multiple topics (science, politics, arts, sports, etc.) - Grades responses as "Correct," "Incorrect," or "Not attempted" - Measures both accuracy of the answers and the model's confidence in it's answer Key Findings: - Larger models show better accuracy in outputs, but there is still significant issues with accuracy - Models tend to overstate their confidence - Higher response consistency correlates with better accuracy The fact that even advanced models like GPT-4 score BELOW 40% on SimpleQA illuminates the concerning gap in AI systems' ability to provide reliable factual information. While the benchmark's narrow focus on short, fact-seeking queries with single, verifiable answers has clear limitations, we are encouraged to see that it at least establishes a measurable baseline for assessing a language model's level of factual accuracy. Hopefully this metric is a starting place that serves as both a reality check and catalyst for developing more trustworthy AI models. Link in the comments for more details and OpenAI's published paper and more information about the case in Alaska. AI for Education #responsibleAI #GenAI #ailiteracy

𝘠𝘰𝘶𝘳 𝘎𝘗𝘛 𝘮𝘪𝘨𝘩𝘵 𝘸𝘰𝘳𝘬 𝘪𝘯 𝘵𝘩𝘦𝘰𝘳𝘺, 𝘣𝘶𝘵 𝘸𝘪𝘭𝘭 𝘪𝘵 𝘴𝘶𝘳𝘷𝘪𝘷𝘦 𝘳𝘦𝘢𝘭𝘪𝘵𝘺? 7 𝘴𝘵𝘦𝘱𝘴 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘳𝘰𝘤𝘬-𝘴𝘰𝘭𝘪𝘥 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴. 𝐖𝐡𝐞𝐧 𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 𝐟𝐨𝐫 𝐆𝐏𝐓 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐫𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. 𝐀 𝐠𝐨𝐨𝐝 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐬𝐦𝐚𝐫𝐭. 𝐈𝐭’𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐮𝐧𝐝𝐞𝐫 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞. Here’s how to make your benchmarks stronger: 1) 𝐂𝐫𝐞𝐚𝐭𝐞 𝐯𝐚𝐫𝐢𝐞𝐝 𝐭𝐞𝐬𝐭 𝐜𝐚𝐬𝐞𝐬 ↳ Mix factual, reasoning, and creative tasks. ↳ Use diverse literacy, domain knowledge, and cultural profiles. ↳ Include short, vague, and adversarial inputs. 2) 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐝𝐞𝐭𝐚𝐢𝐥𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭 𝐫𝐮𝐛𝐫𝐢𝐜 ↳ Check for correctness and logical flow. ↳ Ensure tone matches user needs and context. ↳ Assess for bias, safety, and relevance. 3) 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐞 𝐫𝐞𝐚𝐥 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨𝐬 ↳ Handle frustration, ambiguity, and mistakes. ↳ Adapt to jargon, slang, and emotional cues. ↳ Test both beginners and advanced users. 4) 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 ↳ Look for topic continuity and smooth transitions. ↳ Prioritize direct, timely, and empathetic responses. ↳ Test how well past interactions are remembered. 5) 𝐃𝐞𝐬𝐢𝐠𝐧 "𝐖𝐡𝐚𝐭 𝐈𝐟" 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ↳ What if the user gives incorrect information? ↳ What if the question is vague? ↳ What if cultural sensitivity is required? 6) 𝐂𝐡𝐞𝐜𝐤 𝐚𝐝𝐯𝐞𝐫𝐬𝐚𝐫𝐢𝐚𝐥 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 ↳ Test against tricky or biased questions. ↳ Spot privacy-violating or inappropriate inputs. ↳ Assess ability to reject unethical requests. 7) 𝐀𝐝𝐚𝐩𝐭 𝐟𝐨𝐫 𝐮𝐧𝐢𝐪𝐮𝐞 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 ↳ Tailor benchmarks to customer service, learning, or financial tasks. ↳ Ensure robustness across unexpected “what if” cases. ↳ Refine tests to mirror real user behaviors. Benchmarking isn’t about perfection. It’s about preparation. What benchmarks will you test today? This is something I learned from taking a course and in interviews companies are asking. ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation.