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All of the labor 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 as much as expectations. It’s the fastest approach to lose stakeholders’ interest—or worse, their trust.
On this edition of the Variable, we deal with the very 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 seek out an approach that suits your current needs. Let’s dive in.
LLM Evaluations: from Prototype to Production
Undecided where or learn how to start? Mariya Mansurova presents a comprehensive guide, which walks us through the end-to-end strategy of constructing an evaluation system for LLM products — from assessing early prototypes to implementing continuous quality monitoring in production.
Learn how to Benchmark DeepSeek-R1 Distilled Models on GPQA
Leveraging Ollama and OpenAI’s simple-evals, Kenneth Leung explains learn how to assess the reasoning capabilities of models based on DeepSeek.
Benchmarking Tabular Reinforcement Learning Algorithms
Learn learn how to run experiments within the context of RL agents: Oliver S unpacks the inner workings of multiple algorithms and the way they stack up against one another.
Other Advisable Reads
Why not explore other topics this week, too? our lineup includes smart takes on AI ethics, survival evaluation, and more:
- James O’Brien reflects on an increasingly thorny query: how should human users treat AI agents trained to emulate human emotions?
- Tackling the same topic from a special angle, Marina Tosic wonders who we should always blame when LLM-powered tools produce poor outcomes or encourage bad decisions.
- Survival evaluation isn’t only for calculating health risks or mechanical failure. Samuele Mazzanti shows that it might probably be equally relevant in a business context.
- Using the flawed sort of log can create major issues when interpreting results. Ngoc Doan explains how that happens—and learn how to avoid some common pitfalls.
- How has the arrival of ChatGPT modified the best way we learn latest skills? Reflecting on her own journey in programming, Livia Ellen argues that it’s time for a brand new paradigm.
Meet Our Latest Authors
Don’t miss the work of a few of our newest contributors:
- Chenxiao Yang presents an exciting latest paper on the elemental limits of Chain of Thought-based test-time scaling.
- Thomas Martin Lange is a researcher on the intersection of agricultural sciences, informatics, and data science.
We love publishing articles from latest authors, so in case you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
