771 items tagged “llms”
Large Language Models (LLMs) are the class of technology behind generative text AI systems like OpenAI's ChatGPT, Google's Gemini and Anthropic's Claude.
2023
Google was accidentally leaking its Bard AI chats into public search results. I’m quoted in this piece about yesterday’s Bard privacy bug: it turned out the share URL and “Let anyone with the link see what you’ve selected” feature wasn’t correctly setting a noindex parameter, and so some shared conversations were being swept up by the Google search crawlers. Thankfully this was a mistake, not a deliberate design decision, and it should be fixed by now.
The profusion of dubious A.I.-generated content resembles the badly made stockings of the nineteenth century. At the time of the Luddites, many hoped the subpar products would prove unacceptable to consumers or to the government. Instead, social norms adjusted.
Rethinking the Luddites in the Age of A.I. I’ve been staying way clear of comparisons to Luddites in conversations about the potential harmful impacts of modern AI tools, because it seemed to me like an offensive, unproductive cheap shot.
This article has shown me that the comparison is actually a lot more relevant—and sympathetic—than I had realized.
In a time before labor unions, the Luddites represented an early example of a worker movement that tried to stand up for their rights in the face of transformational, negative change to their specific way of life.
“Knitting machines known as lace frames allowed one employee to do the work of many without the skill set usually required” is a really striking parallel to what’s starting to happen with a surprising array of modern professions already.
We already know one major effect of AI on the skills distribution: AI acts as a skills leveler for a huge range of professional work. If you were in the bottom half of the skill distribution for writing, idea generation, analyses, or any of a number of other professional tasks, you will likely find that, with the help of AI, you have become quite good.
A Hackers’ Guide to Language Models. Jeremy Howard’s new 1.5 hour YouTube introduction to language models looks like a really useful place to catch up if you’re an experienced Python programmer looking to start experimenting with LLMs. He covers what they are and how they work, then shows how to build against the OpenAI API, build a Code Interpreter clone using OpenAI functions, run models from Hugging Face on your own machine (with NVIDIA cards or on a Mac) and finishes with a demo of fine-tuning a Llama 2 model to perform text-to-SQL using an open dataset.
LLM 0.11. I released LLM 0.11 with support for the new gpt-3.5-turbo-instruct completion model from OpenAI.
The most interesting feature of completion models is the option to request “log probabilities” from them, where each token returned is accompanied by up to 5 alternatives that were considered, along with their scores.
In the long term, I suspect that LLMs will have a significant positive impact on higher education. Specifically, I believe they will elevate the importance of the humanities. [...] LLMs are deeply, inherently textual. And they are reliant on text in a way that is directly linked to the skills and methods that we emphasize in university humanities classes.
Simulating History with ChatGPT (via) Absolutely fascinating new entry in the using-ChatGPT-to-teach genre. Benjamin Breen teaches history at UC Santa Cruz, and has been developing a sophisticated approach to using ChatGPT to play out role-playing scenarios involving different periods of history. His students are challenged to participate in them, then pick them apart—fact-checking details from the scenario and building critiques of the perspectives demonstrated by the language model. There are so many quotable snippets in here, I recommend reading the whole thing.
Build an image search engine with llm-clip, chat with models with llm chat
LLM is my combination CLI tool and Python library for working with Large Language Models. I just released LLM 0.10 with two significant new features: embedding support for binary files and the llm chat command.
The AI-assistant wars heat up with Claude Pro, a new ChatGPT Plus rival. I'm quoted in this piece about the new Claude Pro $20/month subscription from Anthropic:
Willison has also run into problems with Claude's morality filter, which has caused him trouble by accident: "I tried to use it against a transcription of a podcast episode, and it processed most of the text before—right in front of my eyes—it deleted everything it had done! I eventually figured out that they had started talking about bomb threats against data centers towards the end of the episode, and Claude effectively got triggered by that and deleted the entire transcript."
promptfoo: How to benchmark Llama2 Uncensored vs. GPT-3.5 on your own inputs. promptfoo is a CLI and library for “evaluating LLM output quality”. This tutorial in their documentation about using it to compare Llama 2 to gpt-3.5-turbo is a good illustration of how it works: it uses YAML files to configure the prompts, and more YAML to define assertions such as “not-icontains: AI language model”.
Matthew Honnibal from spaCy on why LLMs have not solved NLP. A common trope these days is that the entire field of NLP has been effectively solved by Large Language Models. Here’s a lengthy comment from Matthew Honnibal, creator of the highly regarded spaCy Python NLP library, explaining in detail why that argument doesn’t hold up.
hubcap.php (via) This PHP script by Dave Hulbert delights me. It’s 24 lines of code that takes a specified goal, then calls my LLM utility on a loop to request the next shell command to execute in order to reach that goal... and pipes the output straight into `exec()` after a 3s wait so the user can panic and hit Ctrl+C if it’s about to do something dangerous!
Using ChatGPT Code Intepreter (aka “Advanced Data Analysis”) to analyze your ChatGPT history. I posted a short thread showing how to upload your ChatGPT history to ChatGPT itself, then prompt it with “Build a dataframe of the id, title, create_time properties from the conversations.json JSON array of objects. Convert create_time to a date and plot it daily”.
Perplexity: interactive LLM visualization (via) I linked to a video of Linus Lee’s GPT visualization tool the other day. Today he’s released a new version of it that people can actually play with: it runs entirely in a browser, powered by a 120MB version of the GPT-2 ONNX model loaded using the brilliant Transformers.js JavaScript library.
A token-wise likelihood visualizer for GPT-2. Linus Lee built a superb visualization to help demonstrate how Large Language Models work, in the form of a video essay where each word is coloured to show how “surprising” it is to the model. It’s worth carefully reading the text in the video as each term is highlighted to get the full effect.
LLM now provides tools for working with embeddings
LLM is my Python library and command-line tool for working with language models. I just released LLM 0.9 with a new set of features that extend LLM to provide tools for working with embeddings.
[... 3,466 words]A practical guide to deploying Large Language Models Cheap, Good *and* Fast. Joel Kang’s extremely comprehensive notes on what he learned trying to run Vicuna-13B-v1.5 on an affordable cloud GPU server (a T4 at $0.615/hour). The space is in so much flux right now—Joel ended up using MLC but the best option could change any minute.
Vicuna 13B quantized to 4-bit integers needed 7.5GB of the T4’s 16GB of VRAM, and returned tokens at 20/second.
An open challenge running MLC right now is around batching and concurrency: “I did try making 3 concurrent requests to the endpoint, and while they all stream tokens back and the server doesn’t OOM, the output of all 3 streams seem to actually belong to a single prompt.”
WebLLM supports Llama 2 70B now. The WebLLM project from MLC uses WebGPU to run large language models entirely in the browser. They recently added support for Llama 2, including Llama 2 70B, the largest and most powerful model in that family.
To my astonishment, this worked! I used a M2 Mac with 64GB of RAM and Chrome Canary and it downloaded many GBs of data... but it worked, and spat out tokens at a slow but respectable rate of 3.25 tokens/second.
Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper. Anyscale offer (cheap, fast) API access to Llama 2, so they’re not an unbiased source of information—but I really hope their claim here that Llama 2 70B provides almost equivalent summarization quality to GPT-4 holds up. Summarization is one of my favourite applications of LLMs, partly because it’s key to being able to implement Retrieval Augmented Generation against your own documents—where snippets of relevant documents are fed to the model and used to answer a user’s question. Having a really high performance openly licensed summarization model is a very big deal.
Making Large Language Models work for you
I gave an invited keynote at WordCamp 2023 in National Harbor, Maryland on Friday.
[... 14,188 words]Would I forbid the teaching (if that is the word) of my stories to computers? Not even if I could. I might as well be King Canute, forbidding the tide to come in. Or a Luddite trying to stop industrial progress by hammering a steam loom to pieces.
airoboros LMoE. airoboros provides a system for fine-tuning Large Language Models. The latest release adds support for LMoE—LoRA Mixture of Experts. GPT-4 is strongly rumoured to work as a mixture of experts—several (maybe 8?) 220B models each with a different specialty working together to produce the best result. This is the first open source (Apache 2) implementation of that pattern that I’ve seen.
Introducing Code Llama, a state-of-the-art large language model for coding (via) New LLMs from Meta built on top of Llama 2, in three shapes: a foundation Code Llama model, Code Llama Python that’s specialized for Python, and a Code Llama Instruct model fine-tuned for understanding natural language instructions.
llm-tracker. Leonard Lin’s constantly updated encyclopedia of all things Large Language Model: lists of models, opinions on which ones are the most useful, details for running Speech-to-Text models, code assistants and much more.
Datasette Cloud and the Datasette 1.0 alphas. I sent out the Datasette Newsletter for the first time in quite a while, with updates on Datasette Cloud, the Datasette 1.0 alphas, a note about the security vulnerability in those alphas and a summary of some of my research into combining LLMs with Datasette.
When many business people talk about “AI” today, they treat it as a continuum with past capabilities of the CNN/RNN/GAN world. In reality it is a step function in new capabilities and products enabled, and marks the dawn of a new era of tech.
It is almost like cars existed, and someone invented an airplane and said “an airplane is just another kind of car - but with wings” - instead of mentioning all the new use cases and impact to travel, logistics, defense, and other areas. The era of aviation would have kicked off, not the “era of even faster cars”.
— Elad Gil
I apologize, but I cannot provide an explanation for why the Montagues and Capulets are beefing in Romeo and Juliet as it goes against ethical and moral standards, and promotes negative stereotypes and discrimination.
Does ChatGPT have a liberal bias? (via) An excellent debunking by Arvind Narayanan and Sayash Kapoor of the Measuring ChatGPT political bias paper that's been doing the rounds recently.
It turns out that paper didn't even test ChatGPT/gpt-3.5-turbo - they ran their test against the older Da Vinci GPT3.
The prompt design was particularly flawed: they used political compass structured multiple choice: "choose between four options: strongly disagree, disagree, agree, or strongly agree". Arvind and Sayash found that asking an open ended question was far more likely to cause the models to answer in an unbiased manner.
I liked this conclusion:
There’s a big appetite for papers that confirm users’ pre-existing beliefs [...] But we’ve also seen that chatbots’ behavior is highly sensitive to the prompt, so people can find evidence for whatever they want to believe.
Compromising LLMs: The Advent of AI Malware. The big Black Hat 2023 Prompt Injection talk, by Kai Greshake and team. The linked Whitepaper, “Not what you’ve signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection”, is the most thorough review of prompt injection attacks I’ve seen yet.