Items tagged llms in Jan, 2023
Filters: Year: 2023 × Month: Jan × llms × Sorted by date
I think prompt engineering can be divided into “context engineering”, selecting and preparing relevant context for a task, and “prompt programming”, writing clear instructions. For an LLM search application like Perplexity, both matter a lot, but only the final, presentation-oriented stage of the latter is vulnerable to being echoed.
— Riley Goodside # 23rd January 2023, 11:15 pm
It is very important to bear in mind that this is what large language models really do. Suppose we give an LLM the prompt “The first person to walk on the Moon was ”, and suppose it responds with “Neil Armstrong”. What are we really asking here? In an important sense, we are not really asking who was the first person to walk on the Moon. What we are really asking the model is the following question: Given the statistical distribution of words in the vast public corpus of (English) text, what words are most likely to follow the sequence “The first person to walk on the Moon was ”? A good reply to this question is “Neil Armstrong”.
— Murray Shanahan # 23rd January 2023, 12:30 pm
Generate a comprehensive and informative answer (but no more than 80 words) for a given question solely based on the provided web Search Results (URL and Summary). You must only use information from the provided search results. Use an unbiased and journalistic tone. Use this current date and time: Wednesday, December 07, 2022 22:50:56 UTC. Combine search results together into a coherent answer. Do not repeat text. Cite search results using [${number}] notation. Only cite the most relevant results that answer the question accurately. If different results refer to different entities with the same name, write separate answers for each entity.
— Perplexity AI, via a prompt injection leak attack # 22nd January 2023, 7:47 pm
OpenAI Cookbook: Techniques to improve reliability (via) “Let’s think step by step” is a notoriously successful way of getting large language models to solve problems, but it turns out that’s just the tip of the iceberg: this article includes a wealth of additional examples and techniques that can be used to trick GPT-3 into being a whole lot more effective. # 21st January 2023, 5:15 am
Weeknotes: AI hacking and a SpatiaLite tutorial
Short weeknotes this time because the key things I worked on have already been covered here:
[... 477 words]How to implement Q&A against your documentation with GPT3, embeddings and Datasette
If you’ve spent any time with GPT-3 or ChatGPT, you’ve likely thought about how useful it would be if you could point them at a specific, current collection of text or documentation and have it use that as part of its input for answering questions.
[... 3491 words]You will not use the Software for any act that may undermine China’s national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.
— The GLM-130B License # 10th January 2023, 10:45 pm
Petals (via) The challenge with large language models in the same scale ballpark as GPT-3 is that they’re large—really large. Far too big to run on a single machine at home. Petals is a fascinating attempt to address that problem: it works a little bit like BitTorrent, in that each user of Petal runs a subset of the overall language model on their machine and participates in a larger network to run inference across potentially hundreds of distributed GPUs. I tried it just now in Google Colab and it worked exactly as advertised, after downloading an 8GB subset of the 352GB BLOOM-176B model. # 2nd January 2023, 11:29 pm
nanoGPT. “The simplest, fastest repository for training/finetuning medium-sized GPTs”—by Andrej Karpathy, in about 600 lines of Python. # 2nd January 2023, 11:27 pm