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All the hard work it takes to integrate large language models and powerful algorithms into your workflows can go to waste if the outputs you see don’t live up to expectations. It’s the fastest way to lose stakeholders’ interest—or worse, their trust.
In this edition of the Variable, we focus on the best strategies for evaluating and benchmarking the performance of ML approaches, whether it’s a cutting-edge reinforcement learning algorithm or a recently unveiled Llm. We invite you to explore these standout articles to find an approach that suits your current needs. Let’s dive in.
LLM Evaluations: from Prototype to Production
Not sure where or how to start? Mariya Mansurova presents a comprehensive guide, which walks us through the end-to-end process of building an evaluation system for LLM products — from assessing early prototypes to implementing continuous quality monitoring in production.
How to Benchmark DeepSeek-R1 Distilled Models on GPQA
Leveraging Ollama and OpenAI’s simple-evals, Kenneth Leung explains how to assess the reasoning capabilities of models based on DeepSeek.
Benchmarking Tabular Reinforcement Learning Algorithms
Learn how to run experiments in the context of RL agents: Oliver S unpacks the inner workings of multiple algorithms and how they stack up against each other.
Other Recommended Reads
Why not explore other topics this week, too? our lineup includes smart takes on AI ethics, survival analysis, and more:
- James O’Brien reflects on an increasingly thorny question: how should human users treat AI agents trained to emulate human emotions?
- Tackling a similar topic from a different angle, Marina Tosic wonders who we should blame when LLM-powered tools produce poor outcomes or inspire bad decisions.
- Survival analysis isn’t just for calculating health risks or mechanical failure. Samuele Mazzanti shows that it can be equally relevant in a business context.
- Using the wrong type of log can create major issues when interpreting results. Ngoc Doan explains how that happens—and how to avoid some common pitfalls.
- How has the arrival of ChatGPT changed the way we learn new skills? Reflecting on her own journey in programming, Livia Ellen argues that it’s time for a new paradigm.
Meet Our New Authors
Don’t miss the work of some of our newest contributors:
- Chenxiao Yang presents an exciting new paper on the fundamental limits of Chain of Thought-based test-time scaling.
- Thomas Martin Lange is a researcher at the intersection of agricultural sciences, informatics, and data science.
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
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