Almost Timely News: How Large Language Models Work (2023-10-01) :: View in Browser
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What’s On My Mind: How Large Language Models Work – A New Explanation
I’ve been working on a more thorough way of explaining how large language models do what they do. Previously, I had explained how large amounts of text were digested down into statistical representations, and while this is accurate and true, it’s hard for people to visualize. So let’s tackle this in a new way, with word clouds. Now, to be clear, this is a vast oversimplification of the mathematics behind language models. If you enjoy calculus and linear algebra and want to dig into the actual mechanics and mathematics of large language models, I recommend reading the academic paper that started it all, “Attention is All You Need” by Vaswani et al.
Take any word, and there are words associated with it. For example, if I give you the word marketing, what other words related to it come to mind? Digital marketing, content marketing, email marketing, marketing strategy, marketing plans, marketing template, sales and marketing – the list goes on and on, but there are plenty of words that are associated with the word marketing. Imagine that word, marketing, and the words associated with it as a word cloud. The words that occur the most around marketing are bigger in the cloud. Got it?
Next, let’s take a different word, a word like B2B. When you think of words associated with B2B, what words come to mind? B2B marketing, sure. B2B sales, B2B commerce, B2B strategy, and so on and so forth. Again, picture that word and all its associated words as a word cloud and again, the words that occur the most around B2B are bigger in the word cloud.
Now, imagine those two clouds next to each other. What words do they have in common? How much do they overlap and intersect? B2B and marketing share common words in each other’s clouds like sales, commerce, strategy, etc. Those words have an increased probability when you mash the two clouds together, so you could imagine those words would get even bigger.
And that’s the start of how large language models do what they do. Large language models essentially are composed of massive numbers of word clouds for every word they’ve seen, and the words associated with those words. Unlike the toy example we just showed, the way these models are made, each individual word’s cloud is composed of tens or hundreds of thousands of additional words. In the largest models, like GPT-4, there might even be millions of associations for any given word, and those associations also occur among words, phrases, sentences, and even entire documents.
For example, there will be multiple associations for a word – apple could refer to a fruit or a computer company, and the words around apple determine which association will be used. Each of these clusters of association exist inside a large language model as well, which is how it knows to mention Steve Jobs if your prompt contains both apple and computer along with other related words, even if you don’t mention Steve Jobs by name.
When you use a tool like LM Studio or ChatGPT or Google Bard, and you give it a prompt, it goes into its library of word clouds and takes each word from your prompt, extracts the relevant word cloud associations, mashes them all together, and the intersections of all those words are essentially what it spits out as its answer, formatted in the language of your choice. This is why these tools are so effective and so powerful – they have a knowledge of language based on how a word relates to every other word that’s nearby it in millions of pages of text.
This is also what makes the difference between good prompts and bad prompts, between non-creative and creative responses. Think about it for a second. If you write a short, boring prompt, it’s going to create a mash of word clouds that is relatively small, and that means only the most frequent (and therefore boring and non-creative) words will be returned. “Write a blog post about the benefits of email marketing” is going to generate some really mediocre, boring content because it’s a mediocre, boring prompt that will return high-level word cloud mashups only. True, there will still be hundreds of thousands of words in the combined cloud of a prompt that small, but because we’re thinking about the INTERSECTIONS of those clouds, where they overlap, you’re not going to get much variety or creativity:
If you used a prompt like “You are a MarketingProfs B2B Forum award-winning blogger who writes about B2B marketing and email marketing for the industrial concrete industry. Your first task is to draft a blog post about the benefits of a high-frequency email marketing program for an industrial concrete company that sells to state and local governments; focus on unique aspects of marketing the concrete industry and heavy construction. You know CASL, CAN-SPAM, and GDPR. You know email marketing best practices, especially for nurture campaigns in marketing automation systems. Write in a warm, professional tone of voice. Avoid tropes, jargon, and business language. Avoid adverbs.” How many of these word clouds will be created with a prompt this large? Many, many word clouds, and each cloud of associations will have overlaps with the others. The net result is you’ll get a much more tailored, unique, and creative result.
When you understand conceptually what’s going on under the hood of large language models, it becomes easier to understand how to use them to the best of their capabilities – and why non-language tasks simply fail most of the time. For example, math is really hard for many models to get right because they fundamentally don’t do computation. They’re predicting the likelihood of characters – numbers – and the numbers that should be nearby. That’s why earlier models had no trouble with expressions like 2 + 2 = 4 but could not do 22 + 7 = 29. The former equation occurs much more frequently in written text, while the latter is fairly rare by comparison. The model isn’t performing any calculations, and thus tends to get the answer wrong.
This is also why censorship is so damaging to the structure of these models. Take any common profane word, like the venerable F word. How often do we use it? How many other words are associated with it? If you were to try ripping it out of a combination of word clouds, how many other words might get ripped out too – and are they useful words otherwise?
That’s also why models behave less or more creatively. They’re not intrinsically creative; they’re simply clouds of probabilities being mashed together. When you give an non-creative prompt, you invoke only the most broad probabilities, and you get a non-creative result. When you give a highly creative, relatively rare prompt that has many combinations of many specific words, you invoke very specific probabilities and get more creative results.
Large language models are libraries of probability, and every time we use them, we are invoking probabilities based on the words in our prompts. If we aren’t getting the results we want, we should examine the words, phrases, and sentences in our prompts and adjust them to add more detail until we get what we want. There’s no magic formula or secret guide to prompt engineering, no “Instant Success with ChatGPT” that has any serious credibility. If you have conversations with these models that use the appropriate language to get all the word clouds to overlap well, you’ll get what you want from a large language model.
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ICYMI: In Case You Missed it
Besides the newly-refreshed Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I recommend the episode I did with Katie on how to vet an analytics agency.
- In-Ear Insights: How to Vet and Hire an Analytics Agency or Employee
- You Ask, I Answer: Who To Hire For Large Language Models?
- Almost Timely News, September 24, 2023: AI and the Rise of the Ideapreneur
- You Ask, I Answer: Why Do Open Source Large Language Models Matter?
- You Ask, I Answer: Power of Large Language Models?
- You Ask, I Answer: Adoption of Generative AI and Impact?
- iOS 17 Is Coming To Break All Your Tracking!
- So What? Crisis Comms and Generative AI
Skill Up With Classes
These are just a few of the classes I have available over at the Trust Insights website that you can take.
- 👉 Google Analytics 4 for Marketers
- 👉 Google Search Console for Marketers (🚨 just updated with AI SEO stuff! 🚨)
- ⭐️ The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It
- Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition
- Measurement Strategies for Agencies
- Empower Your Marketing With Private Social Media Communities
- Exploratory Data Analysis: The Missing Ingredient for AI
- How AI is Changing Marketing, 2022 Edition
- How to Prove Social Media ROI
- Proving Social Media ROI
- Paradise by the Analytics Dashboard Light: How to Create Impactful Dashboards and Reports
Advertisement: Bring My AI Talk To Your Company
I’ve been lecturing a lot on large language models and generative AI (think ChatGPT) lately, and inevitably, there’s far more material than time permits at a regular conference keynote. There’s a lot more value to be unlocked – and that value can be unlocked by bringing me in to speak at your company. In a customized version of my AI keynote talk, delivered either in-person or virtually, we’ll cover all the high points of the talk, but specific to your industry, and critically, offer a ton of time to answer your specific questions that you might not feel comfortable asking in a public forum.
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Get Back to Work
Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.
- Analyst Relations Director at Braze
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- Data Analyst at Indeed.com
- Developer Experience Engineer at LaunchDarkly
- Director, Product Marketing, Data at Braze
- Senior Data Engineer – Marketing Platform at Confused.com
- Senior Digital Marketing Manager at Braze
- Vp, Product Marketing at Braze
What I’m Reading: Your Stuff
Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.
Social Media Marketing
- Watch Linda Yaccarino’s wild interview at the Code Conference – The Verge
- What Is Shadow Work, and Why Is It All Over TikTok? via The Atlantic
- Twitter/X is biggest source of social media disinformation, EU warns
Media and Content
- Earned Media vs. Paid Media: The Truth In No Upfront Fees
- Managing pan-European and global PR networks via Vitis PR
- Brands keep glomming onto Taylor Swift, with mixed results via PR Daily
SEO, Google, and Paid Media
- Google Concludes Rollout Of September 2023 Helpful Content Update
- Google uses Click Data for Rankings – SISTRIX
- Benchmarking SEO: Do a competitive analysis Yoast
Advertisement: Business Cameos
If you’re familiar with the Cameo system – where people hire well-known folks for short video clips – then you’ll totally get Thinkers One. Created by my friend Mitch Joel, Thinkers One lets you connect with the biggest thinkers for short videos on topics you care about. I’ve got a whole slew of Thinkers One Cameo-style topics for video clips you can use at internal company meetings, events, or even just for yourself. Want me to tell your boss that you need to be paying attention to generative AI right now?
Tools, Machine Learning, and AI
- Meta quietly releases Llama 2 Long AI model via VentureBeat
- As Meta brings AI to apps, Google Bard’s fail offers cautionary tale via VentureBeat
- Google Offers Publishers Control Over Bard, Vertex AI Access
Analytics, Stats, and Data Science
- Data Preprocessing in Machine Learning: A Beginner’s Guide
- How to Develop A Multi-File Chatbot? via Analytics Vidhya
- 5 Google Analytics Reports Every PPC Marketer Needs To Know
All Things IBM
- Child support systems modernization: The time is now via IBM Blog
- Unleashing the power of Presto: The Uber case study via IBM Blog
- Spatial computing is the next frontier in airline flight safety via IBM Blog
Dealer’s Choice : Random Stuff
- Why Activision Blizzard Media is using an Attention Measurement Scorecard to raise marketers’ confidence in gaming via Digiday
- Log on the iPhone 15 Pro: a big upgrade by Iain Anderson – ProVideo Coalition
- Secret Service Agent Rates 11 POTUS Protection Scenes In Movies And TV | How Real Is It?
How to Stay in Touch
Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:
- My blog – daily videos, blog posts, and podcast episodes
- My YouTube channel – daily videos, conference talks, and all things video
- My company, Trust Insights – marketing analytics help
- My podcast, Marketing over Coffee – weekly episodes of what’s worth noting in marketing
- My second podcast, In-Ear Insights – the Trust Insights weekly podcast focused on data and analytics
- On Threads – random personal stuff and chaos
- On LinkedIn – daily videos and news
- On Instagram – personal photos and travels
- My free Slack discussion forum, Analytics for Marketers – open conversations about marketing and analytics
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Events I’ll Be At
Here’s where I’m speaking and attending. Say hi if you’re at an event also:
- MarketingProfs B2B Forum, Boston, October 2023
- Content Jam, Chicago, October 2023
- SMPS AEC AI, DC, October 2023
- Humanize Your Brand, Online, October 2023
- AI and the End of SEO with SiteImprove, Online, October 2023
- DigitalNow, Denver, November 2023
- AImpact, Online, November 2023
- Social Media Marketing World, San Diego, February 2024
Events marked with a physical location may become virtual if conditions and safety warrant it.
If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.
Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.
Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.
Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.
My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.
Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.
See you next week,
Christopher S. Penn
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