45 items tagged “training-data”
Data used to train LLMs and other machine learning models.
2024
Phi-4 Technical Report (via) Phi-4 is the latest LLM from Microsoft Research. It has 14B parameters and claims to be a big leap forward in the overall Phi series. From Introducing Phi-4: Microsoft’s Newest Small Language Model Specializing in Complex Reasoning:
Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations. Phi-4 continues to push the frontier of size vs quality.
The model is currently available via Azure AI Foundry. I couldn't figure out how to access it there, but Microsoft are planning to release it via Hugging Face in the next few days. It's not yet clear what license they'll use - hopefully MIT, as used by the previous models in the series.
In the meantime, unofficial GGUF versions have shown up on Hugging Face already. I got one of the matteogeniaccio/phi-4 GGUFs working with my LLM tool and llm-gguf plugin like this:
llm install llm-gguf
llm gguf download-model https://huggingface.co/matteogeniaccio/phi-4/resolve/main/phi-4-Q4_K_M.gguf
llm chat -m gguf/phi-4-Q4_K_M
This downloaded a 8.4GB model file. Here are some initial logged transcripts I gathered from playing around with the model.
An interesting detail I spotted on the Azure AI Foundry page is this:
Limited Scope for Code: Majority of phi-4 training data is based in Python and uses common packages such as
typing,math,random,collections,datetime,itertools. If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
This leads into the most interesting thing about this model: the way it was trained on synthetic data. The technical report has a lot of detail about this, including this note about why synthetic data can provide better guidance to a model:
Synthetic data as a substantial component of pretraining is becoming increasingly common, and the Phi series of models has consistently emphasized the importance of synthetic data. Rather than serving as a cheap substitute for organic data, synthetic data has several direct advantages over organic data.
Structured and Gradual Learning. In organic datasets, the relationship between tokens is often complex and indirect. Many reasoning steps may be required to connect the current token to the next, making it challenging for the model to learn effectively from next-token prediction. By contrast, each token generated by a language model is by definition predicted by the preceding tokens, making it easier for a model to follow the resulting reasoning patterns.
And this section about their approach for generating that data:
Our approach to generating synthetic data for phi-4 is guided by the following principles:
- Diversity: The data should comprehensively cover subtopics and skills within each domain. This requires curating diverse seeds from organic sources.
- Nuance and Complexity: Effective training requires nuanced, non-trivial examples that reflect the complexity and the richness of the domain. Data must go beyond basics to include edge cases and advanced examples.
- Accuracy: Code should execute correctly, proofs should be valid, and explanations should adhere to established knowledge, etc.
- Chain-of-Thought: Data should encourage systematic reasoning, teaching the model various approaches to the problems in a step-by-step manner. [...]
We created 50 broad types of synthetic datasets, each one relying on a different set of seeds and different multi-stage prompting procedure, spanning an array of topics, skills, and natures of interaction, accumulating to a total of about 400B unweighted tokens. [...]
Question Datasets: A large set of questions was collected from websites, forums, and Q&A platforms. These questions were then filtered using a plurality-based technique to balance difficulty. Specifically, we generated multiple independent answers for each question and applied majority voting to assess the consistency of responses. We discarded questions where all answers agreed (indicating the question was too easy) or where answers were entirely inconsistent (indicating the question was too difficult or ambiguous). [...]
Creating Question-Answer pairs from Diverse Sources: Another technique we use for seed curation involves leveraging language models to extract question-answer pairs from organic sources such as books, scientific papers, and code.
Meta AI release Llama 3.3. This new Llama-3.3-70B-Instruct model from Meta AI makes some bold claims:
This model delivers similar performance to Llama 3.1 405B with cost effective inference that’s feasible to run locally on common developer workstations.
I have 64GB of RAM in my M2 MacBook Pro, so I'm looking forward to trying a slightly quantized GGUF of this model to see if I can run it while still leaving some memory free for other applications.
Update: Ollama have a 43GB GGUF available now. And here's an MLX 8bit version and other MLX quantizations.
Llama 3.3 has 70B parameters, a 128,000 token context length and was trained to support English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
The model card says that the training data was "A new mix of publicly available online data" - 15 trillion tokens with a December 2023 cut-off.
They used "39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware" which they calculate as 11,390 tons CO2eq. I believe that's equivalent to around 20 fully loaded passenger flights from New York to London (at ~550 tons per flight).
Amazon Bedrock doesn't store or log your prompts and completions. Amazon Bedrock doesn't use your prompts and completions to train any AWS models and doesn't distribute them to third parties.
New Pleias 1.0 LLMs trained exclusively on openly licensed data (via) I wrote about the Common Corpus public domain dataset back in March. Now Pleias, the team behind Common Corpus, have released the first family of models that are:
[...] trained exclusively on open data, meaning data that are either non-copyrighted or are published under a permissible license.
There's a lot to absorb here. The Pleias 1.0 family comes in three base model sizes: 350M, 1.2B and 3B. They've also released two models specialized for multi-lingual RAG: Pleias-Pico (350M) and Pleias-Nano (1.2B).
Here's an official GGUF for Pleias-Pico.
I'm looking forward to seeing benchmarks from other sources, but Pleias ran their own custom multilingual RAG benchmark which had their Pleias-nano-1.2B-RAG model come in between Llama-3.2-Instruct-3B and Llama-3.2-Instruct-8B.
The 350M and 3B models were trained on the French government's Jean Zay supercomputer. Pleias are proud of their CO2 footprint for training the models - 0.5, 4 and 16 tCO2eq for the three models respectively, which they compare to Llama 3.2,s reported figure of 133 tCO2eq.
How clean is the training data from a licensing perspective? I'm confident people will find issues there - truly 100% public domain data remains a rare commodity. So far I've seen questions raised about the GitHub source code data (most open source licenses have attribution requirements) and Wikipedia (CC BY-SA, another attribution license). Plus this from the announcement:
To supplement our corpus, we have generated 30B+ words synthetically with models allowing for outputs reuse.
If those models were themselves trained on unlicensed data this could be seen as a form of copyright laundering.
OK, I can partly explain the LLM chess weirdness now
(via)
Last week Dynomight published Something weird is happening with LLMs and chess pointing out that most LLMs are terrible chess players with the exception of gpt-3.5-turbo-instruct (OpenAI's last remaining completion as opposed to chat model, which they describe as "Similar capabilities as GPT-3 era models").
After diving deep into this, Dynomight now has a theory. It's mainly about completion models v.s. chat models - a completion model like gpt-3.5-turbo-instruct naturally outputs good next-turn suggestions, but something about reformatting that challenge as a chat conversation dramatically reduces the quality of the results.
Through extensive prompt engineering Dynomight got results out of GPT-4o that were almost as good as the 3.5 instruct model. The two tricks that had the biggest impact:
- Examples. Including just three examples of inputs (with valid chess moves) and expected outputs gave a huge boost in performance.
- "Regurgitation" - encouraging the model to repeat the entire sequence of previous moves before outputting the next move, as a way to help it reconstruct its context regarding the state of the board.
They experimented a bit with fine-tuning too, but I found their results from prompt engineering more convincing.
No non-OpenAI models have exhibited any talents for chess at all yet. I think that's explained by the A.2 Chess Puzzles section of OpenAI's December 2023 paper Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision:
The GPT-4 pretraining dataset included chess games in the format of move sequence known as Portable Game Notation (PGN). We note that only games with players of Elo 1800 or higher were included in pretraining.
The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing, programming, question answering). Qwen 2.5-Coder sees them train this model on an additional 5.5 trillion tokens of data. This means Qwen has been trained on a total of ~23T tokens of data – for perspective, Facebook’s LLaMa3 models were trained on about 15T tokens. I think this means Qwen is the largest publicly disclosed number of tokens dumped into a single language model (so far).
Releasing the largest multilingual open pretraining dataset (via) Common Corpus is a new "open and permissible licensed text dataset, comprising over 2 trillion tokens (2,003,039,184,047 tokens)" released by French AI Lab PleIAs.
This appears to be the largest available corpus of openly licensed training data:
- 926,541,096,243 tokens of public domain books, newspapers, and Wikisource content
- 387,965,738,992 tokens of government financial and legal documents
- 334,658,896,533 tokens of open source code from GitHub
- 221,798,136,564 tokens of academic content from open science repositories
- 132,075,315,715 tokens from Wikipedia, YouTube Commons, StackExchange and other permissively licensed web sources
It's majority English but has significant portions in French and German, and some representation for Latin, Dutch, Italian, Polish, Greek and Portuguese.
I can't wait to try some LLMs trained exclusively on this data. Maybe we will finally get a GPT-4 class model that isn't trained on unlicensed copyrighted data.
Who called it “intellectual property problems around the acquisition of training data for Large Language Models” and not Grand Theft Autocomplete?
— Jens Ohlig, on March 8th 2024
Gemini API Additional Terms of Service. I've been trying to figure out what Google's policy is on using data submitted to their Google Gemini LLM for further training. It turns out it's clearly spelled out in their terms of service, but it differs for the paid v.s. free tiers.
The paid APIs do not train on your inputs:
When you're using Paid Services, Google doesn't use your prompts (including associated system instructions, cached content, and files such as images, videos, or documents) or responses to improve our products [...] This data may be stored transiently or cached in any country in which Google or its agents maintain facilities.
The Gemini API free tier does:
The terms in this section apply solely to your use of Unpaid Services. [...] Google uses this data, consistent with our Privacy Policy, to provide, improve, and develop Google products and services and machine learning technologies, including Google’s enterprise features, products, and services. To help with quality and improve our products, human reviewers may read, annotate, and process your API input and output.
But watch out! It looks like the AI Studio tool, since it's offered for free (even if you have a paid account setup) is treated as "free" for the purposes of these terms. There's also an interesting note about the EU:
The terms in this "Paid Services" section apply solely to your use of paid Services ("Paid Services"), as opposed to any Services that are offered free of charge like direct interactions with Google AI Studio or unpaid quota in Gemini API ("Unpaid Services"). [...] If you're in the European Economic Area, Switzerland, or the United Kingdom, the terms applicable to Paid Services apply to all Services including AI Studio even though it's offered free of charge.
Confusingly, the following paragraph about data used to fine-tune your own custom models appears in that same "Data Use for Unpaid Services" section:
Google only uses content that you import or upload to our model tuning feature for that express purpose. Tuning content may be retained in connection with your tuned models for purposes of re-tuning when supported models change. When you delete a tuned model, the related tuning content is also deleted.
It turns out their tuning service is "free of charge" on both pay-as-you-go and free plans according to the Gemini pricing page, though you still pay for input/output tokens at inference time (on the paid tier - it looks like the free tier remains free even for those fine-tuned models).
I think individual creators or publishers tend to overestimate the value of their specific content in the grand scheme of [AI training]. […]
We pay for content when it’s valuable to people. We’re just not going to pay for content when it’s not valuable to people. I think that you’ll probably see a similar dynamic with AI, which my guess is that there are going to be certain partnerships that get made when content is really important and valuable. I’d guess that there are probably a lot of people who have a concern about the feel of it, like you’re saying. But then, when push comes to shove, if they demanded that we don’t use their content, then we just wouldn’t use their content. It’s not like that’s going to change the outcome of this stuff that much.
Debate over “open source AI” term brings new push to formalize definition. Benj Edwards reports on the latest draft (v0.0.9) of a definition for "Open Source AI" from the Open Source Initiative.
It's been under active development for around a year now, and I think the definition is looking pretty solid. It starts by emphasizing the key values that make an AI system "open source":
An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to:
- Use the system for any purpose and without having to ask for permission.
- Study how the system works and inspect its components.
- Modify the system for any purpose, including to change its output.
- Share the system for others to use with or without modifications, for any purpose.
These freedoms apply both to a fully functional system and to discrete elements of a system. A precondition to exercising these freedoms is to have access to the preferred form to make modifications to the system.
There is one very notable absence from the definition: while it requires the code and weights be released under an OSI-approved license, the training data itself is exempt from that requirement.
At first impression this is disappointing, but I think it it's a pragmatic decision. We still haven't seen a model trained entirely on openly licensed data that's anywhere near the same class as the current batch of open weight models, all of which incorporate crawled web data or other proprietary sources.
For the OSI definition to be relevant, it needs to acknowledge this unfortunate reality of how these models are trained. Without that, we risk having a definition of "Open Source AI" that none of the currently popular models can use!
Instead of requiring the training information, the definition calls for "data information" described like this:
Data information: Sufficiently detailed information about the data used to train the system, so that a skilled person can recreate a substantially equivalent system using the same or similar data. Data information shall be made available with licenses that comply with the Open Source Definition.
The OSI's FAQ that accompanies the draft further expands on their reasoning:
Training data is valuable to study AI systems: to understand the biases that have been learned and that can impact system behavior. But training data is not part of the preferred form for making modifications to an existing AI system. The insights and correlations in that data have already been learned.
Data can be hard to share. Laws that permit training on data often limit the resharing of that same data to protect copyright or other interests. Privacy rules also give a person the rightful ability to control their most sensitive information – like decisions about their health. Similarly, much of the world’s Indigenous knowledge is protected through mechanisms that are not compatible with later-developed frameworks for rights exclusivity and sharing.
Leaked Documents Show Nvidia Scraping ‘A Human Lifetime’ of Videos Per Day to Train AI.
Samantha Cole at 404 Media reports on a huge leak of internal NVIDIA communications - mainly from a Slack channel - revealing details of how they have been collecting video training data for a new video foundation model called Cosmos. The data is mostly from YouTube, downloaded via yt-dlp using a rotating set of AWS IP addresses and consisting of millions (maybe even hundreds of millions) of videos.
The fact that companies scrape unlicensed data to train models isn't at all surprising. This article still provides a fascinating insight into what model training teams care about, with details like this from a project update via email:
As we measure against our desired distribution focus for the next week remains on cinematic, drone footage, egocentric, some travel and nature.
Or this from Slack:
Movies are actually a good source of data to get gaming-like 3D consistency and fictional content but much higher quality.
My intuition here is that the backlash against scraped video data will be even more intense than for static images used to train generative image models. Video is generally more expensive to create, and video creators (such as Marques Brownlee / MKBHD, who is mentioned in a Slack message here as a potential source of "tech product neviews - super high quality") have a lot of influence.
There was considerable uproar a few weeks ago over this story about training against just captions scraped from YouTube, and now we have a much bigger story involving the actual video content itself.
SAM 2: The next generation of Meta Segment Anything Model for videos and images (via) Segment Anything is Meta AI's model for image segmentation: for any image or frame of video it can identify which shapes on the image represent different "objects" - things like vehicles, people, animals, tools and more.
SAM 2 "outperforms SAM on its 23 dataset zero-shot benchmark suite, while being six times faster". Notably, SAM 2 works with video where the original SAM only worked with still images. It's released under the Apache 2 license.
The best way to understand SAM 2 is to try it out. Meta have a web demo which worked for me in Chrome but not in Firefox. I uploaded a recent video of my brand new cactus tweezers (for removing detritus from my cacti without getting spiked) and selected the succulent and the tweezers as two different objects:
Then I applied a "desaturate" filter to the background and exported this resulting video, with the background converted to black and white while the succulent and tweezers remained in full colour:
Also released today: the full SAM 2 paper, the SA-V dataset of "51K diverse videos and 643K spatio-temporal segmentation masks" and a Dataset explorer tool (again, not supported by Firefox) for poking around in that collection.
The [Apple Foundation Model] pre-training dataset consists of a diverse and high quality data mixture. This includes data we have licensed from publishers, curated publicly-available or open-sourced datasets, and publicly available information crawled by our web-crawler, Applebot. We respect the right of webpages to opt out of being crawled by Applebot, using standard robots.txt directives.
Given our focus on protecting user privacy, we note that no private Apple user data is included in the data mixture. Additionally, extensive efforts have been made to exclude profanity, unsafe material, and personally identifiable information from publicly available data (see Section 7 for more details). Rigorous decontamination is also performed against many common evaluation benchmarks.
We find that data quality, much more so than quantity, is the key determining factor of downstream model performance.
The reason current models are so large is because we're still being very wasteful during training - we're asking them to memorize the internet and, remarkably, they do and can e.g. recite SHA hashes of common numbers, or recall really esoteric facts. (Actually LLMs are really good at memorization, qualitatively a lot better than humans, sometimes needing just a single update to remember a lot of detail for a long time). But imagine if you were going to be tested, closed book, on reciting arbitrary passages of the internet given the first few words. This is the standard (pre)training objective for models today. The reason doing better is hard is because demonstrations of thinking are "entangled" with knowledge, in the training data.
Therefore, the models have to first get larger before they can get smaller, because we need their (automated) help to refactor and mold the training data into ideal, synthetic formats.
It's a staircase of improvement - of one model helping to generate the training data for next, until we're left with "perfect training set". When you train GPT-2 on it, it will be a really strong / smart model by today's standards. Maybe the MMLU will be a bit lower because it won't remember all of its chemistry perfectly.
Apple, Nvidia, Anthropic Used Thousands of Swiped YouTube Videos to Train AI. This article has been getting a lot of attention over the past couple of days.
The story itself is nothing new: the Pile is four years old now, and has been widely used for training LLMs since before anyone even cared what an LLM was. It turns out one of the components of the Pile is a set of ~170,000 YouTube video captions (just the captions, not the actual video) and this story by Annie Gilbertson and Alex Reisner highlights that and interviews some of the creators who were included in the data, as well as providing a search tool for seeing if a specific creator has content that was included.
What's notable is the response. Marques Brownlee (19m subscribers) posted a video about it. Abigail Thorn (Philosophy Tube, 1.57m subscribers) tweeted this:
Very sad to have to say this - an AI company called EleutherAI stole tens of thousands of YouTube videos - including many of mine. I’m one of the creators Proof News spoke to. The stolen data was sold to Apple, Nvidia, and other companies to build AI
When I was told about this I lay on the floor and cried, it’s so violating, it made me want to quit writing forever. The reason I got back up was because I know my audience come to my show for real connection and ideas, not cheapfake AI garbage, and I know they’ll stay with me
Framing the data as "sold to Apple..." is a slight misrepresentation here - EleutherAI have been giving the Pile away for free since 2020. It's a good illustration of the emotional impact here though: many creative people do not want their work used in this way, especially without their permission.
It's interesting seeing how attitudes to this stuff change over time. Four years ago the fact that a bunch of academic researchers were sharing and training models using 170,000 YouTube subtitles would likely not have caught any attention at all. Today, people care!
Why The Atlantic signed a deal with OpenAI. Interesting conversation between Nilay Patel and The Atlantic CEO (and former journalist/editor) Nicholas Thompson about the relationship between media organizations and LLM companies like OpenAI.
On the impact of these deals on the ongoing New York Times lawsuit:
One of the ways that we [The Atlantic] can help the industry is by making deals and setting a market. I believe that us doing a deal with OpenAI makes it easier for us to make deals with the other large language model companies if those come about, I think it makes it easier for other journalistic companies to make deals with OpenAI and others, and I think it makes it more likely that The Times wins their lawsuit.
How could it help? Because deals like this establish a market value for training content, important for the fair use component of the legal argument.
Listen to the AI-generated ripoff songs that got Udio and Suno sued. Jason Koebler reports on the lawsuit filed today by the RIAA against Udio and Suno, the two leading generative music startups.
The lawsuit includes examples of prompts that the record labels used to recreate famous songs that were almost certainly included in the (undisclosed) training data. Jason collected some of these together into a three minute video, and the result in pretty damning. Arguing "fair use" isn't going to be easy here.
It is in the public good to have AI produce quality and credible (if ‘hallucinations’ can be overcome) output. It is in the public good that there be the creation of original quality, credible, and artistic content. It is not in the public good if quality, credible content is excluded from AI training and output OR if quality, credible content is not created.
Extracting Concepts from GPT-4. A few weeks ago Anthropic announced they had extracted millions of understandable features from their Claude 3 Sonnet model.
Today OpenAI are announcing a similar result against GPT-4:
We used new scalable methods to decompose GPT-4’s internal representations into 16 million oft-interpretable patterns.
These features are "patterns of activity that we hope are human interpretable". The release includes code and a paper, Scaling and evaluating sparse autoencoders paper (PDF) which credits nine authors, two of whom - Ilya Sutskever and Jan Leike - are high profile figures that left OpenAI within the past month.
The most fun part of this release is the interactive tool for exploring features. This highlights some interesting features on the homepage, or you can hit the "I'm feeling lucky" button to bounce to a random feature. The most interesting I've found so far is feature 5140 which seems to combine God's approval, telling your doctor about your prescriptions and information passed to the Admiralty.
This note shown on the explorer is interesting:
Only 65536 features available. Activations shown on The Pile (uncopyrighted) instead of our internal training dataset.
Here's the full Pile Uncopyrighted, which I hadn't seen before. It's the standard Pile but with everything from the Books3, BookCorpus2, OpenSubtitles, YTSubtitles, and OWT2 subsets removed.
Turns out that LLMs learn a lot better and faster from educational content as well. This is partly because the average Common Crawl article (internet pages) is not of very high value and distracts the training, packing in too much irrelevant information. The average webpage on the internet is so random and terrible it's not even clear how prior LLMs learn anything at all.
Training is not the same as chatting: ChatGPT and other LLMs don’t remember everything you say
I’m beginning to suspect that one of the most common misconceptions about LLMs such as ChatGPT involves how “training” works.
[... 1,543 words]I’ve been at OpenAI for almost a year now. In that time, I’ve trained a lot of generative models. [...] It’s becoming awfully clear to me that these models are truly approximating their datasets to an incredible degree. [...] What this manifests as is – trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point. [...] This is a surprising observation! It implies that model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else. Everything else is a means to an end in efficiently delivery compute to approximating that dataset.
openelm/README-pretraining.md. Apple released something big three hours ago, and I’m still trying to get my head around exactly what it is.
The parent project is called CoreNet, described as “A library for training deep neural networks”. Part of the release is a new LLM called OpenELM, which includes completely open source training code and a large number of published training checkpoint.
I’m linking here to the best documentation I’ve found of that training data: it looks like the bulk of it comes from RefinedWeb, RedPajama, The Pile and Dolma.
Releasing Common Corpus: the largest public domain dataset for training LLMs (via) Released today. 500 billion words from “a wide diversity of cultural heritage initiatives”. 180 billion words of English, 110 billion of French, 30 billion of German, then Dutch, Spanish and Italian.
Includes quite a lot of US public domain data—21 million digitized out-of-copyright newspapers (or do they mean newspaper articles?)
“This is only an initial part of what we have collected so far, in part due to the lengthy process of copyright duration verification. In the following weeks and months, we’ll continue to publish many additional datasets also coming from other open sources, such as open data or open science.”
Coordinated by French AI startup Pleias and supported by the French Ministry of Culture, among others.
I can’t wait to try a model that’s been trained on this.
For the last few years, Meta has had a team of attorneys dedicated to policing unauthorized forms of scraping and data collection on Meta platforms. The decision not to further pursue these claims seems as close to waving the white flag as you can get against these kinds of companies. But why? [...]
In short, I think Meta cares more about access to large volumes of data and AI than it does about outsiders scraping their public data now. My hunch is that they know that any success in anti-scraping cases can be thrown back at them in their own attempts to build AI training databases and LLMs. And they care more about the latter than the former.
Aya (via) “A global initiative led by Cohere For AI involving over 3,000 independent researchers across 119 countries. Aya is a state-of-art model and dataset, pushing the boundaries of multilingual AI for 101 languages through open science.”
Both the model and the training data are released under Apache 2. The training data looks particularly interesting: “513 million instances through templating and translating existing datasets across 114 languages”—suggesting the data is mostly automatically generated.
Open Language Models (OLMos) and the LLM landscape (via) OLMo is a newly released LLM from the Allen Institute for AI (AI2) currently available in 7b and 1b parameters (OLMo-65b is on the way) and trained on a fully openly published dataset called Dolma.
The model and code are Apache 2, while the data is under the “AI2 ImpACT license”.
From the benchmark scores shared here by Nathan Lambert it looks like this may be the highest performing model currently available that was built using a fully documented training set.
What’s in Dolma? It’s mainly Common Crawl, Wikipedia, Project Gutenberg and the Stack.
We believe that AI tools are at their best when they incorporate and represent the full diversity and breadth of human intelligence and experience. [...] Because copyright today covers virtually every sort of human expression– including blog posts, photographs, forum posts, scraps of software code, and government documents–it would be impossible to train today’s leading AI models without using copyrighted materials. Limiting training data to public domain books and drawings created more than a century ago might yield an interesting experiment, but would not provide AI systems that meet the needs of today’s citizens.
2023
The AI trust crisis
Dropbox added some new AI features. In the past couple of days these have attracted a firestorm of criticism. Benj Edwards rounds it up in Dropbox spooks users with new AI features that send data to OpenAI when used.
[... 1,733 words]