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Lexin Zhou

@lexinzhou.bsky.social
25 followers 17 following 25 posts

PhD candidate at Princeton | Research on AI Evaluation, Social Computing, AI Safety, RL | lexzhou.github.io

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Lexin Zhou @lexinzhou.bsky.social · 13/05/2025
Thrilled to share this accessible MSR blogpost that summarizes our latest work on building a Science of AI Evaluation, where we manage to both reliably explain and predict success/failure of general-purpose AI models on new, unforeseen tasks and environments!
arxiv.org
General Scales Unlock AI Evaluation with Explanatory and Predictive Power
Ensuring safe and effective use of AI requires understanding and anticipating its performance on novel tasks, from advanced scientific challenges to transformed workplace activities. So far, benchmark...
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Lexin Zhou @lexinzhou.bsky.social · 14/03/2025
🚨To continuously foster conceptual & technical innovations for a science of AI Evaluation: An open collaborative community is initiated by Leverhulme Centre for the Future of Intelligence, to adopt and extend our novel methodology. Join us: kinds-of-intelligence-cfi.github.io/ADELE!
kinds-of-intelligence-cfi.github.io
ADeLe v1.0: A battery for AI Evaluation with explanatory and predictive power
A community dedicated to the use and extension.
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Reposted by Lexin Zhou
Peter Henderson @peterhenderson.bsky.social · 11/03/2025
To better understand why this matters in high-stakes contexts, you can also check out our previous work. We discuss why predicting model performance (e.g., failures on out-of-distribution languages in machine translation) remains essential in legal contexts.
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Reposted by Lexin Zhou
Peter Henderson @peterhenderson.bsky.social · 11/03/2025
Understanding and extrapolating benchmark results will become essential for effective policymaking and informing users. New work identifies indicators that have high predictive power in modeling LLM performance. Excited for it to be out!
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Lexin Zhou @lexinzhou.bsky.social · 11/03/2025
Thrilled to unlock AI Evaluation with explanatory and predictive power through general ability scales! With a new methodology to -Explain what common benchmarks really measure -Extract explainable ability profiles of AI systems -Predict performance for new task instances, in & out-of-distribution 🧵
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