Andrew Fairless – Page 6 – Andrew Fairless, Ph.D.

What I Read: standard error

By Andrew Fairless on October 22, 2024July 22, 2024

http://www.stat.columbia.edu/~gelman/research/published/standarderror.pdf What is a standard error?Andrew Gelman7 September 2023 “In statistics, the standard error has a clear technical definition… In practice, though, challenges arise when we go beyond the simpleContinue readingWhat I Read: standard error

What I Read: LLM evaluation

By Andrew Fairless on October 21, 2024July 22, 2024

https://hamel.dev/blog/posts/evals Your AI Product Needs EvalsHow to construct domain-specific LLM evaluation systems.Hamel HusainMarch 29, 2024 “…I’ve seen many successful and unsuccessful approaches to building LLM products. I’ve found that unsuccessfulContinue readingWhat I Read: LLM evaluation

What I Read: Data Flywheels, LLM

By Andrew Fairless on October 17, 2024July 20, 2024

https://www.sh-reya.com/blog/ai-engineering-flywheel Data Flywheels for LLM ApplicationsShreya ShankarJul 1, 2024 “This diagram illustrates my (idealized) architecture of an LLM pipeline, from input processing through evaluation and logging. It showcases ideas I’llContinue readingWhat I Read: Data Flywheels, LLM

What I Read: Use-cases, inverted PCA

By Andrew Fairless on October 16, 2024July 14, 2024

https://www.youtube.com/watch?v=ZK3_T-UgKWM Use-cases for inverted PCA:probabl.Jul 11, 2024 “A lot of people know of PCA for it’s ability to reduce the dimensionality of a dataset. It can turn a wide datasetContinue readingWhat I Read: Use-cases, inverted PCA

What I Read: Improving Language Models, Practical Size

By Andrew Fairless on October 15, 2024July 14, 2024

https://amaarora.github.io/posts/2024-07-07%20Gemma.html Gemma 2, Improving Open Language Models at a Practical SizeAman AroraJuly 9, 2024 “…we take a deep dive into the architectural components of Gemma 2 such as Grouped QueryContinue readingWhat I Read: Improving Language Models, Practical Size

What I Read: Hidden Infinity, Preference Learning

By Andrew Fairless on October 10, 2024July 14, 2024

https://www.cs.princeton.edu/~smalladi/blog/2024/07/09/dpo-infinity The Hidden Infinity in Preference LearningSadhika MalladiJuly 09 2024 “I demonstrate from first principles how offline preference learning algorithms (e.g., SimPO) can benefit from length normalization, especially when trainingContinue readingWhat I Read: Hidden Infinity, Preference Learning

What I Read: Illustrated AlphaFold

By Andrew Fairless on October 9, 2024July 14, 2024

https://elanapearl.github.io/blog/2024/the-illustrated-alphafold The Illustrated AlphaFoldElana Simon, Jake Silberg “A visual walkthrough of the AlphaFold3 architecture…”

What I Read: Extrinsic Hallucinations, LLMs

By Andrew Fairless on October 7, 2024July 14, 2024

https://lilianweng.github.io/posts/2024-07-07-hallucination Extrinsic Hallucinations in LLMsLilian WengJuly 7, 2024 “This post focuses on extrinsic hallucination. To avoid hallucination, LLMs need to be (1) factual and (2) acknowledge not knowing the answerContinue readingWhat I Read: Extrinsic Hallucinations, LLMs

What I Read: decision analysis, significance testing

By Andrew Fairless on October 3, 2024July 14, 2024

https://statmodeling.stat.columbia.edu/2024/07/10/a-misunderstanding-about-decision-analysis-and-significance-testing (Trying to) clear up a misunderstanding about decision analysis and significance testingAndrew Gelman7/10/24 9:03 AM “…we’re just saying that screening based on statistical significance has lots of problems. P-valuesContinue readingWhat I Read: decision analysis, significance testing

What I Read: What can LLMs never do?

By Andrew Fairless on October 1, 2024July 14, 2024

https://www.strangeloopcanon.com/p/what-can-llms-never-do What can LLMs never do?Rohit KrishnanApr 23, 2024 “Every time over the past few years that we came up with problems LLMs can’t do, they passed them with flyingContinue readingWhat I Read: What can LLMs never do?

Author: Andrew Fairless