natural language processing – Page 3

What I Read: Turing Test, intelligence

By Andrew Fairless on November 12, 2024August 25, 2024

https://www.science.org/doi/10.1126/science.adq9356 The Turing Test and our shifting conceptions of intelligenceMelanie MitchellScienceVol 385, Issue 6710DOI: 10.1126/science.adq935615 Aug 2024 “It’s likely that the Turing Test will become yet another casualty of ourContinue readingWhat I Read: Turing Test, intelligence

What I Read: Generative AI Platform

By Andrew Fairless on October 29, 2024August 3, 2024

https://huyenchip.com//2024/07/25/genai-platform.html Building A Generative AI PlatformChip Huyen7/24/24 “This post outlines the common components of a generative AI platform, what they do, and how they are implemented.”

What I Read: History, Transformer

By Andrew Fairless on October 28, 2024July 22, 2024

Shaping the Future of AI from the History of TransformerStanford CS25: V4 I Hyung Won Chung of OpenAIStanford Online “I will provide a highly-opinionated view on the early history ofContinue readingWhat I Read: History, Transformer

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: 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: 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: 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?

What I Read: Detecting hallucinations, LLMs, semantic entropy

By Andrew Fairless on September 23, 2024July 8, 2024

https://oatml.cs.ox.ac.uk/blog/2024/06/19/detecting_hallucinations_2024.html Detecting hallucinations in large language models using semantic entropySebastian Farquhar, Jannik Kossen, Lorenz Kuhn, Yarin Gal19 Jun 2024 “We show how one can use uncertainty to detect confabulations.”

Tag: natural language processing