Benchmarking the Performance of Large Language Models on the Cerebras Wafer Scale Engine
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Inside the Chaos at OpenAI: Sam Altman’s weekend of shock and drama began a year ago, with the release of ChatGPT
Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
PAIR: Jailbreaking Black Box Large Language Models in 20 Queries
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Large Language Models as Superpositions of Cultural Perspectives
AI Is a Lot of Work: As the technology becomes ubiquitous, a vast tasker underclass is emerging—and not going anywhere
I’m Afraid I Can’t Do That: Predicting Prompt Refusal in Black-Box Generative Language Models
Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
OpenAI’s Sam Altman Talks ChatGPT And How Artificial General Intelligence Can ‘Break Capitalism’
GPT-3 as Knowledge Worker: A Zero-Shot Evaluation of AI CPA Capabilities
Language models are better than humans at next-token prediction
‘How GPT-3 Is Shaping Our AI Future’ With Sam Altman/Azeem Azhar (The Exponential View), Wednesday 7 October 2020
Scaling Laws for Neural Language Models: Figure 15: Far beyond the Model Sizes We Study Empirically, We Find a Contradiction between Our Equations § Pg17
20d126b9c3baf640f8d1d5dff3e253faac2e8242.pdf#page=17&org=openai
Towards Synthesizing Complex Programs from Input-Output Examples
Genetics of caffeine consumption and responses to caffeine
GPT-3 Gives Some Interesting True and False Answers to Some Questions. But It’s Important to Note That It Gives opposite Answers Just As Often, I Cheery Picked the Most ‘Sensational’ Ones. Usually It Said the opposite Thing, and It Also Role-Plays Sometimes (eg. As a Spy)
I Gave GPT-3 Access to Chrome With the Objective ‘Please Buy Me AirPods’...It Successfully Made It to the Product Page, but Got Sidetracked With Walmart’s Privacy Policy. Since Even a Simplified DOM Is Far Too Large for a Single Prompt, Multiple Prompts Are given Different Chunks of the DOM, Each Generating Their Own ‘Interaction’. Another Prompt Then Takes All the Proposed Interactions and Selects the Best One, Sort of like a Tournament Bracket. For More Complex Web Pages, the Time It Takes to Generate an Action Scales at 𝒪(log n) With the Size of the DOM—Really Fast! It Also Gets around Token Limits, so You Could Technically Process an Infinitely Large DOM!
2019-11-07-amodei-aiandcompute-twodistincteras-gpt3modified.jpg
https://andrewmayne.com/2023/11/14/is-the-reversal-curse-real/
https://barryzhang.substack.com/p/our-humble-attempt-at-fine-tuning
https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-updates
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4594466
https://www.cerebras.net/blog/introducing-gigagpt-gpt-3-sized-models-in-565-lines-of-code
https://www.lesswrong.com/posts/CNPvESPru3XNqsw7A/what-s-up-with-all-the-non-mormons-weirdly-specific
https://www.lesswrong.com/posts/c6uTNm5erRrmyJvvD/mapping-the-semantic-void-strange-goings-on-in-gpt-embedding
https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse#pfHTedu4GKaWoxD5K
https://www.lesswrong.com/posts/tJAD2LG9uweeEfjwq/estimating-efficiency-improvements-in-llm-pre-training
https://www.reddit.com/r/mlscaling/comments/146rgq2/chatgpt_is_running_quantized/
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
https%253A%252F%252Farxiv.org%252Fabs%252F2401.08406%2523microsoft.html
Inside the Chaos at OpenAI: Sam Altman’s weekend of shock and drama began a year ago, with the release of ChatGPT
https%253A%252F%252Fwww.theatlantic.com%252Ftechnology%252Farchive%252F2023%252F11%252Fsam-altman-open-ai-chatgpt-chaos%252F676050%252F.html
PAIR: Jailbreaking Black Box Large Language Models in 20 Queries
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
https%253A%252F%252F152334h.github.io%252Fblog%252Fnon-determinism-in-gpt-4%252F.html
Large Language Models as Superpositions of Cultural Perspectives
AI Is a Lot of Work: As the technology becomes ubiquitous, a vast tasker underclass is emerging—and not going anywhere
https%253A%252F%252Fwww.theverge.com%252Ffeatures%252F23764584%252Fai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots.html
I’m Afraid I Can’t Do That: Predicting Prompt Refusal in Black-Box Generative Language Models
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
OpenAI’s Sam Altman Talks ChatGPT And How Artificial General Intelligence Can ‘Break Capitalism’
https%253A%252F%252Fwww.forbes.com%252Fsites%252Falexkonrad%252F2023%252F02%252F03%252Fexclusive-openai-sam-altman-chatgpt-agi-google-search%252F.html
GPT-3 as Knowledge Worker: A Zero-Shot Evaluation of AI CPA Capabilities
Wikipedia Bibliography:
