Hacker News with Generative AI: Evaluation

You're Not Testing Your AI Well Enough (tryreva.com)
Large Language Models (LLMs) have revolutionised machine learning, offering unprecedented versatility across various tasks. However, this flexibility poses a significant challenge: how do we effectively evaluate LLMs to ensure they’re suitable for specific applications?
Generated Checklists Improve LLM Evaluation and Generation (arxiv.org)
Given the widespread adoption and usage of Large Language Models (LLMs), it is crucial to have flexible and interpretable evaluations of their instruction-following ability.
We're approaching LLM prompt evaluation at QA.tech (qa.tech)
The development of autonomous agents poses a unique challenge that other types of applications don’t typically grapple with: heavy reliance on inherently non-deterministic dependencies at multiple points within the system.
Show HN: Anton the Search Relevance Evaluator (objective.inc)
IRL 25: Evaluating Language Models on Life's Curveballs (alignedhq.ai)
Everyone Is Judging AI by These Tests. Experts Say They're Close to Meaningless (themarkup.org)
eBook on building LLM system evals (forestfriends.tech)
Usefulness Grounds Truth (invertedpassion.com)
How to think about creating a dataset for LLM fine-tuning evaluation (mlops.systems)
Show HN: Paramount – Human Evals of AI Customer Support (github.com/ask-fini)
Evaluation of Machine Learning Primitives on a Digital Signal Processor (diva-portal.org)
Measure Schools on Student Growth (brettcvz.com)
Lessons from the trenches on reproducible evaluation of language models (arxiv.org)
I just spent the past 5 hours comparing LLMs (ycombinator.com)
LLM Leaderboard with explanations of what each score means (crfm.stanford.edu)
An unbiased evaluation of Python environment and packaging tools (2023) (alpopkes.com)