Simon Willison’s Weblog


32 items tagged “gpt4”


Extracting data from unstructured text and images with Datasette and GPT-4 Turbo. Datasette Extract is a new Datasette plugin that uses GPT-4 Turbo (released to general availability today) and GPT-4 Vision to extract structured data from unstructured text and images.

I put together a video demo of the plugin in action today, and posted it to the Datasette Cloud blog along with screenshots and a tutorial describing how to use it. # 9th April 2024, 11:03 pm

“The king is dead”—Claude 3 surpasses GPT-4 on Chatbot Arena for the first time. I’m quoted in this piece by Benj Edwards for Ars Technica:

“For the first time, the best available models—Opus for advanced tasks, Haiku for cost and efficiency—are from a vendor that isn’t OpenAI. That’s reassuring—we all benefit from a diversity of top vendors in this space. But GPT-4 is over a year old at this point, and it took that year for anyone else to catch up.” # 27th March 2024, 4:58 pm

In every group I speak to, from business executives to scientists, including a group of very accomplished people in Silicon Valley last night, much less than 20% of the crowd has even tried a GPT-4 class model.

Less than 5% has spent the required 10 hours to know how they tick.

Ethan Mollick # 9th March 2024, 3:55 am

The GPT-4 barrier has finally been broken

Four weeks ago, GPT-4 remained the undisputed champion: consistently at the top of every key benchmark, but more importantly the clear winner in terms of “vibes”. Almost everyone investing serious time exploring LLMs agreed that it was the most capable default model for the majority of tasks—and had been for more than a year.

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Inflection-2.5: meet the world’s best personal AI (via) I’ve not been paying much attention to Inflection’s Pi since it released last year, but yesterday they released a new version that they claim is competitive with GPT-4.

“Inflection-2.5 approaches GPT-4’s performance, but used only 40% of the amount of compute for training.”

(I wasn’t aware that the compute used to train GPT-4 was public knowledge.)

If this holds true, that means that the GPT-4 barrier has been well and truly smashed: we now have Claude 3 Opus, Gemini 1.5, Mistral Large and Inflection-2.5 in the same class as GPT-4, up from zero contenders just a month ago. # 8th March 2024, 12:51 am

The new Claude 3 model family from Anthropic. Claude 3 is out, and comes in three sizes: Opus (the largest), Sonnet and Haiku.

Claude 3 Opus has self-reported benchmark scores that consistently beat GPT-4. This is a really big deal: in the 12+ months since the GPT-4 release no other model has consistently beat it in this way. It’s exciting to finally see that milestone reached by another research group.

The pricing model here is also really interesting. Prices here are per-million-input-tokens / per-million-output-tokens:

Claude 3 Opus: $15 / $75
Claude 3 Sonnet: $3 / $15
Claude 3 Haiku: $0.25 / $1.25

All three models have a 200,000 length context window and support image input in addition to text.

Compare with today’s OpenAI prices:

GPT-4 Turbo (128K): $10 / $30
GPT-4 8K: $30 / $60
GPT-4 32K: $60 / $120
GPT-3.5 Turbo: $0.50 / $1.50

So Opus pricing is comparable with GPT-4, more than GPT-4 Turbo and significantly cheaper than GPT-4 32K... Sonnet is cheaper than all of the GPT-4 models (including GPT-4 Turbo), and Haiku (which has not yet been released to the Claude API) will be cheaper even than GPT-3.5 Turbo.

It will be interesting to see if OpenAI respond with their own price reductions. # 4th March 2024, 6:34 pm

Google’s Gemini Advanced: Tasting Notes and Implications. Ethan Mollick reviews the new Google Gemini Advanced—a rebranded Bard, released today, that runs on the GPT-4 competitive Gemini Ultra model.

“GPT-4 [...] has been the dominant AI for well over a year, and no other model has come particularly close. Prior to Gemini, we only had one advanced AI model to look at, and it is hard drawing conclusions with a dataset of one. Now there are two, and we can learn a few things.”

I like Ethan’s use of the term “tasting notes” here. Reminds me of how Matt Webb talks about being a language model sommelier. # 8th February 2024, 3:10 pm


gpt-4-turbo over the API produces (statistically significant) shorter completions when it “thinks” its December vs. when it thinks its May (as determined by the date in the system prompt).

I took the same exact prompt over the API (a code completion task asking to implement a machine learning task without libraries).

I created two system prompts, one that told the API it was May and another that it was December and then compared the distributions.

For the May system prompt, mean = 4298
For the December system prompt, mean = 4086

N = 477 completions in each sample from May and December

t-test p < 2.28e-07

Rob Lynch # 11th December 2023, 7:45 pm

Mixtral of experts (via) Mistral have firmly established themselves as the most exciting AI lab outside of OpenAI, arguably more exciting because much of their work is released under open licenses.

On December 8th they tweeted a link to a torrent, with no additional context (a neat marketing trick they’ve used in the past). The 87GB torrent contained a new model, Mixtral-8x7b-32kseqlen—a Mixture of Experts.

Three days later they published a full write-up, describing “Mixtral 8x7B, a high-quality sparse mixture of experts model (SMoE) with open weights”—licensed Apache 2.0.

They claim “Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference”—and that it outperforms GPT-3.5 on most benchmarks too.

This isn’t even their current best model. The new Mistral API platform (currently on a waitlist) refers to Mixtral as “Mistral-small” (and their previous 7B model as “Mistral-tiny”—and also provides access to a currently closed model, “Mistral-medium”, which they claim to be competitive with GPT-4. # 11th December 2023, 5:20 pm

When I speak in front of groups and ask them to raise their hands if they used the free version of ChatGPT, almost every hand goes up. When I ask the same group how many use GPT-4, almost no one raises their hand. I increasingly think the decision of OpenAI to make the “bad” AI free is causing people to miss why AI seems like such a huge deal to a minority of people that use advanced systems and elicits a shrug from everyone else.

Ethan Mollick # 10th December 2023, 8:17 pm

Ice Cubes GPT-4 prompts. The Ice Cubes open source Mastodon app recently grew a very good “describe this image” feature to help people add alt text to their images. I had a dig around in their repo and it turns out they’re using GPT-4 Vision for this (and regular GPT-4 for other features), passing the image with this prompt:

“What’s in this image? Be brief, it’s for image alt description on a social network. Don’t write in the first person.” # 6th December 2023, 7:38 pm

tldraw/draw-a-ui (via) Absolutely spectacular GPT-4 Vision API demo. Sketch out a rough UI prototype using the open source tldraw drawing app, then select a set of components and click “Make Real” (after giving it an OpenAI API key). It generates a PNG snapshot of your selection and sends that to GPT-4 with instructions to turn it into a Tailwind HTML+JavaScript prototype, then adds the result as an iframe next to your mockup.

You can then make changes to your mockup, select it and the previous mockup and click “Make Real” again to ask for an updated version that takes your new changes into account.

This is such a great example of innovation at the UI layer, and everything is open source. Check app/lib/getHtmlFromOpenAI.ts for the system prompt that makes it work. # 16th November 2023, 4:42 pm

ospeak: a CLI tool for speaking text in the terminal via OpenAI

I attended OpenAI DevDay today, the first OpenAI developer conference. It was a lot. They released a bewildering array of new API tools, which I’m just beginning to wade my way through fully understanding.

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Multi-modal prompt injection image attacks against GPT-4V

GPT4-V is the new mode of GPT-4 that allows you to upload images as part of your conversations. It’s absolutely brilliant. It also provides a whole new set of vectors for prompt injection attacks.

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Translating Latin demonology manuals with GPT-4 and Claude (via) UC Santa Cruz history professor Benjamin Breen puts LLMs to work on historical texts. They do an impressive job of translating flaky OCRd text from 1599 Latin and 1707 Portuguese.

“It’s not about getting the AI to replace you. Instead, it’s asking the AI to act as a kind of polymathic research assistant to supply you with leads.” # 4th October 2023, 1:49 am

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. # 30th August 2023, 2:37 pm

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. # 24th August 2023, 10:31 pm

Study claims ChatGPT is losing capability, but some experts aren’t convinced. Benj Edwards talks about the ongoing debate as to whether or not GPT-4 is getting weaker over time. I remain skeptical of those claims—I think it’s more likely that people are seeing more of the flaws now that the novelty has worn off.

I’m quoted in this piece: “Honestly, the lack of release notes and transparency may be the biggest story here. How are we meant to build dependable software on top of a platform that changes in completely undocumented and mysterious ways every few months?” # 20th July 2023, 12:22 am

OpenAI: Function calling and other API updates. Huge set of announcements from OpenAI today. A bunch of price reductions, but the things that most excite me are the new gpt-3.5-turbo-16k model which offers a 16,000 token context limit (4x the existing 3.5 turbo model) at a price of $0.003 per 1K input tokens and $0.004 per 1K output tokens—1/10th the price of GPT-4 8k.

The other big new feature: functions! You can now send JSON schema defining one or more functions to GPT 3.5 and GPT-4—those models will then return a blob of JSON describing a function they want you to call (if they determine that one should be called). Your code executes the function and passes the results back to the model to continue the execution flow.

This is effectively an implementation of the ReAct pattern, with models that have been fine-tuned to execute it.

They acknowledge the risk of prompt injection (though not by name) in the post: “We are working to mitigate these and other risks. Developers can protect their applications by only consuming information from trusted tools and by including user confirmation steps before performing actions with real-world impact, such as sending an email, posting online, or making a purchase.” # 13th June 2023, 5:34 pm

Understanding GPT tokenizers

Large language models such as GPT-3/4, LLaMA and PaLM work in terms of tokens. They take text, convert it into tokens (integers), then predict which tokens should come next.

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Examples of weird GPT-4 behavior for the string “ davidjl”. GPT-4, when told to repeat or otherwise process the string “ davidjl” (note the leading space character), treats it as “jndl” or “jspb” or “JDL” instead. It turns out “ davidjl” has its own single token in the tokenizer: token ID 23282, presumably dating back to the GPT-2 days.

Riley Goodside refers to these as “glitch tokens”.

This token might refer to Reddit user davidjl123 who ranks top of the league for the old /r/counting subreddit, with 163,477 posts there which presumably ended up in older training data. # 8th June 2023, 9:29 am

Language models can explain neurons in language models (via) Fascinating interactive paper by OpenAI, describing how they used GPT-4 to analyze the concepts tracked by individual neurons in their much older GPT-2 model. “We generated cluster labels by embedding each neuron explanation using the OpenAI Embeddings API, then clustering them and asking GPT-4 to label each cluster.” # 9th May 2023, 5:35 pm

Although fine-tuning can feel like the more natural option—training on data is how GPT learned all of its other knowledge, after all—we generally do not recommend it as a way to teach the model knowledge. Fine-tuning is better suited to teaching specialized tasks or styles, and is less reliable for factual recall. [...] In contrast, message inputs are like short-term memory. When you insert knowledge into a message, it’s like taking an exam with open notes. With notes in hand, the model is more likely to arrive at correct answers.

Ted Sanders, OpenAI # 15th April 2023, 1:44 pm

Closed AI Models Make Bad Baselines (via) The NLP academic research community are facing a tough challenge: the state-of-the-art in large language models, GPT-4, is entirely closed which means papers that compare it to other models lack replicability and credibility. “We make the case that as far as research and scientific publications are concerned, the “closed” models (as defined below) cannot be meaningfully studied, and they should not become a “universal baseline”, the way BERT was for some time widely considered to be.”

Anna Rogers proposes a new rule for this kind of research: “That which is not open and reasonably reproducible cannot be considered a requisite baseline.” # 3rd April 2023, 7:57 pm

scrapeghost (via) Scraping is a really interesting application for large language model tools like GPT3. James Turk’s scrapeghost is a very neatly designed entrant into this space—it’s a Python library and CLI tool that can be pointed at any URL and given a roughly defined schema (using a neat mini schema language) which will then use GPT3 to scrape the page and try to return the results in the supplied format. # 26th March 2023, 5:29 am

GPT-4, like GPT-3 before it, has a capability overhang; at the time of release, neither OpenAI or its various deployment partners have a clue as to the true extent of GPT-4’s capability surface—that’s something that we’ll get to collectively discover in the coming years. This also means we don’t know the full extent of plausible misuses or harms.

Jack Clark # 22nd March 2023, 12:40 am

As an NLP researcher I’m kind of worried about this field after 10-20 years. Feels like these oversized LLMs are going to eat up this field and I’m sitting in my chair thinking, “What’s the point of my research when GPT-4 can do it better?”

Jeonghwan Kim # 16th March 2023, 5:39 am

I expect GPT-4 will have a LOT of applications in web scraping

The increased 32,000 token limit will be large enough to send it the full DOM of most pages, serialized to HTML—then ask questions to extract data

Or... take a screenshot and use the GPT4 image input mode to ask questions about the visually rendered page instead!

Might need to dust off all of those old semantic web dreams, because the world’s information is rapidly becoming fully machine readable

Me # 16th March 2023, 1:09 am

GPT-4 Developer Livestream. 25 minutes of live demos from OpenAI co-founder Greg Brockman at the GPT-4 launch. These demos are all fascinating, including code writing and multimodal vision inputs. The one that really struck me is when Greg pasted in a copy of the tax code and asked GPT-4 to answer some sophisticated tax questions, involving step-by-step calculations that cited parts of the tax code it was working with. # 15th March 2023, 12:20 am

GPT-4 Technical Report (PDF). 98 pages of much more detailed information about GPT-4. The appendices are particularly interesting, including examples of advanced prompt engineering as well as examples of harmful outputs before and after tuning attempts to try and suppress them. # 14th March 2023, 9:39 pm