large language model – Page 10 – Andrew Fairless, Ph.D.

What I Read: evaluating AI systems

By Andrew Fairless on December 5, 2023October 12, 2023

https://www.anthropic.com/index/evaluating-ai-systems Challenges in evaluating AI systemsOct 4, 2023 “…what many people working inside and outside of AI don’t fully appreciate is how difficult it is to build robust and reliableContinue readingWhat I Read: evaluating AI systems

What I Read: Problems of AI Consciousness

By Andrew Fairless on December 4, 2023October 5, 2023

https://thegradient.pub/an-introduction-to-the-problems-of-ai-consciousness/ An Introduction to the Problems of AI ConsciousnessNick Alonso9/30/23 “This brief introduction is aimed at those working within the AI community who are interested in AI consciousness, but mayContinue readingWhat I Read: Problems of AI Consciousness

What I Read: Privacy side channels in ML

By Andrew Fairless on November 27, 2023October 5, 2023

https://spylab.ai/blog/side-channels-machine-learning/ Privacy side channels in machine learning systemsEdoardo Debenedetti, Florian TramèrSep 12, 2023 “An additional application of our membership inference attack is to test whether specific data sources were partContinue readingWhat I Read: Privacy side channels in ML

What I Read: Estimate Token Importance in LLM Prompts

By Andrew Fairless on November 16, 2023October 5, 2023

https://www.watchful.io/blog/a-surprisingly-effective-way-to-estimate-token-importance-in-llm-prompts A Surprisingly Effective Way to Estimate Token Importance in LLM PromptsShayan Mohanty “Prompting is our primary means of instructing these AIs, yet the process of crafting effective prompts hasContinue readingWhat I Read: Estimate Token Importance in LLM Prompts

What I Read: How make history with LLMs

By Andrew Fairless on November 8, 2023October 5, 2023

https://leighmariebraswell.substack.com/p/how-to-make-history-with-llms-and How to make history with LLMs & other generative modelsLeigh Marie BraswellSep 21, 2023 “Over the past year I’ve talked to many founders, operators, and investors, and I’ve encounteredContinue readingWhat I Read: How make history with LLMs

What I Read: Optimizing LLM in production

By Andrew Fairless on November 6, 2023October 5, 2023

https://huggingface.co/blog/optimize-llm Optimizing your LLM in productionSeptember 15, 2023Patrick von Platen “…efficient LLM deployment…. pros and cons of adopting lower precision, provide a comprehensive exploration of the latest attention algorithms, andContinue readingWhat I Read: Optimizing LLM in production

What I Read: LLM Training, RLHF

By Andrew Fairless on October 30, 2023October 5, 2023

https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives LLM Training: RLHF and Its AlternativesSebastian Raschka, PhDSep 10, 2023 “RLHF is an integral part of the modern LLM training pipeline due to its ability to incorporate human preferencesContinue readingWhat I Read: LLM Training, RLHF

What I Read: LLMs, single example

By Andrew Fairless on October 23, 2023September 9, 2023

https://www.fast.ai/posts/2023-09-04-learning-jumps/ Can LLMs learn from a single example?Jeremy Howard and Jonathan WhitakerSeptember 4, 2023 “We’ve noticed an unusual training pattern in fine-tuning LLMs. At first we thought it’s a bug,Continue readingWhat I Read: LLMs, single example

What I Read: GPT-4, 8 Models in One

By Andrew Fairless on October 12, 2023September 4, 2023

https://www.kdnuggets.com/2023/08/gpt4-8-models-one-secret.html GPT-4: 8 Models in One; The Secret is OutBy Dr. Mandar Karhade, MD. PhD., Sr. Director Data Strategy and Advanced Analytics at Avalere HealthAugust 24, 2023 “The Mixture ofContinue readingWhat I Read: GPT-4, 8 Models in One

What I Read: LLM research

By Andrew Fairless on October 10, 2023September 4, 2023

https://huyenchip.com//2023/08/16/llm-research-open-challenges.html Open challenges in LLM research8/15/23 8:00 PM “Never before in my life had I seen so many smart people working on the same goal: making LLMs better… I noticedContinue readingWhat I Read: LLM research

Tag: large language model