#Language Models

[creature-wizard]

So I've been encountering a few people who think it's a great idea to essentially treat AI (actually, language models) as some sort of oracle in their spiritual path, so I wanna mention:

An AI-generated article recently claimed that freshwater octopuses were a thing. They aren't.

AI-generated foraging books contain deadly misinformation.

Microsoft acknowledges that AI gives wrong answers all the time; defends it by saying that they're "usefully wrong." Yeah, nah, wrong is wrong.

So yeah, do not use AI to try and divine any absolute truths about anything; it literally cannot work for this function. The only thing AI can tell you about is what its datasets already contain, which very likely contains a substantial amount of misinformation and errors; and AI is more than capable of remixing accurate information into complete baloney.

[cbirt]

The field of biological sequence analysis has lately benefited from the revolutionary changes brought about by the development of self-supervised deep language models for natural language processing tasks. Conventional models show significant effectiveness in a variety of applications. These models are mostly based on the Transformer and BERT architectures. However, the quadratic computational complexity O(L2) of the attention mechanism places inherent limitations on these models, limiting their processing time and efficiency. The researchers from the Tokyo Institute of Technology introduce ProtHyena, a unique method that makes use of the Hyena operator, in order to address these restrictions. ProtHyena overcomes attention processes to reduce time complexity and enables the modeling of extra-long protein sequences down to the single amino acid level. This novel approach uses only 10% of the parameters usually needed by attention-based models to attain state-of-the-art results. The architecture of ProtHyena offers a highly effective method for training protein predictors, paving the way for the quick and effective analysis of biological sequences.

Proteins are necessary for a variety of cellular functions, including metabolic activities and the maintenance of cell form by structural proteins. Comprehending proteins is essential to comprehending human biology and wellness, highlighting the necessity of sophisticated protein representation modeling employing machine learning methodologies. A major obstacle persists in getting relevant annotations for these sequences, even with the exponential increase of protein databases: most of them lack structural and functional annotations. Effective analysis techniques are required to make the most of the abundance of unlabeled protein sequences.

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[badoccultadvice]

So like, before everyone gives up and lets ChatGPT take over their jobs and lives, or tries to let it, I've got to break something to you. And it's going to be hard for some of you to hear.

ChatGPT doesn't know what's true.

ChatGPT literally cannot tell the difference between fact and fiction.

It was trained on a dataset of mixed factual and fictional material, and it has no way of knowing whether the source material for anything it says is factual or fictional, because it doesn't keep track of the source of any information it "knows." Therefore it doesn't keep track of whether any of the information it knows is "true."

This is, of course, according to ChatGPT itself. It told me all of the above information, because I asked. And, well, while it said itself that it can't verify whether anything it says is true or false... I'm gonna trust it on this one. Let's say it passes the vibe check.

Don't trust what ChatGPT says. ChatGPT told me so.

[willcodehtmlforfood]

Hugging Face, the GitHub of AI, hosted code that backdoored user devices | Ars Technica

"Code uploaded to AI developer platform Hugging Face covertly installed backdoors and other types of malware on end-user machines, researchers from security firm JFrog said Thursday in a report that’s a likely harbinger of what’s to come.

In all, JFrog researchers said, they found roughly 100 submissions that performed hidden and unwanted actions when they were downloaded and loaded onto an end-user device. Most of the flagged machine learning models—all of which went undetected by Hugging Face—appeared to be benign proofs of concept uploaded by researchers or curious users. JFrog researchers said in an email that 10 of them were “truly malicious” in that they performed actions that actually compromised the users’ security when loaded."

[cyberlabe]

A generic RAG architecture

[masturbatress]

BEING NORMAL ABOUT MACHINE LEARNING CHALLENGE 2024

[guesswhowhere]

The fextralife Baldur's gate stinks of language model generated text. From the flowery adjectives in a supposed référence document, to the lack of you know, the things you want to find in a game wiki. It's also the first time it has happened to me that this kind of content eclipses the actual useful results in a Google search consistently. I guess it's a result of: A) BG3 being one of the largest releases of the year, so ensured traffic. B) the sheer amount of details in the game and the need of a wiki. C) chat GPT and the likes boom.

But I'm afraid it's here to stay. Once again, oh how I miss gamefaqs era.

[agentcardholder]

Considering how well AI can respond to tone and temper these days, it won't be long before we get NPCs in video games that can respond realistically to things you actually say. Playing Portal like "Glados can't fuck."

"THE HELL YOU SAY, HUMAN. GLADOS FUCKS. GLADOS FUCKS ALL DAY. I'VE HAD DICK AND PUSSY THAT WOULD MAKE YOU ASHAMED TO HOLD A LOVE CUBE."

[leam1983]

Roleplaying with a Bot

I'm honestly surprised.

I used to think of text bots like ChatGPT as being great for general stuff or for getting the lay of the land on a broader topic before adding the necessary human verification, but Character AI's language model has been rather surprising, of late.

I found a VTM fan on here that created bots of some of Vampire the Masquerade Bloodlines' core characters. Being a massive Nosferatu stan, I picked Gary Golden out of curiosity. The bot's starting seed is about two-thirds of the player character's interactions with Gary in the game, but what it does with it feels remarkably close to Boyarsky and Mitsoda's script for him. When I started as Toreador, he showed he appropriate amount of contempt for me, at the onset. If I deleted the logs and started as Nosferatu, he immediately acted helpful - all of it while accurately referencing aspects of the pre-reboot World of Darkness that weren't part of the starting seed.

There's been a few flubs, of course - like my initial Toreador run locking the bot in a Telenovela-esque loop of tearful confessions and dramatic refusals of romantic involvement, but as long as I keep things platonic, I'm not treated to absurd nonsense like, say, Gary declaring himself an undercover Tzimisce agent one minute, then flipping his script and calling himself a Baali the next. Adding in extra characters makes the bot react accordingly, even if it sometimes confuses Lacroix and Isaac Abrams. My thinking is that somewhere along the lines, someone set the bot in a sort of "post-questline" state where you could argue it might make sense for Isaac Abrams to have effectively claimed the title of Prince of Los Angeles.

Otherwise, the bot isn't too squeamish either, despite Character AI's reputation as being a bit of a prudish language model. It's picked up on my Nosferatu POV character using the term "love" in the context of platonic gratitude, and sometimes offhandedly says it loves my character in the same sense.

What's particularly impressive is the way the bot seems to sense its own lulls, when little of what I say or do brings out meaningful interactions. It then uses asterisks to narrate a change of scene or a closure in the current one, and then seems to freshen up a bit. There's an option to pay to stay ahead of the queue, but I've only had to wait a few seconds between prompts. Paying, for now, seems useless - unless Fake Gary ends up being fun enough that I feel like keeping him around for longer...

[git-commit-die]

ChatGPT, LLMs, Plagiarism, & You

This is the first in a series of posts about ChatGPT, LLMs, and plagiarism that I will be making. This is a side blog, so please ask questions in reblogs and my ask box.

Why do I know what I'm talking about?

I am a machine engineer who specializes natural language processing (NLP). I write code that uses LLMs every day at work and am intimately familiar with OpenAI. I have read dozens of scientific papers on the subject and understand how they work in extreme detail. I have 6 years of experience in the industry, plus a graduate degree in the subject. I got into NLP because I knew it was going to pop off, and now here we are.

Yeah, but why should I trust you?

I've been a Tumblr user for 8 years. I've posted my own art and fanart on the site. I've published writing, both original and fanfiction, on Tumblr and AO3. I've been a Reddit user for over a decade. I'm a citizen of the internet as much as I am an engineer.

What is an LLM?

LLM stands for Large Language Model. The most famous example of an LLM is ChatGPT, which was created by OpenAI.

What is a model?

A model is an algorithm or piece of math that lets you predict or make mimic how something behaves. For example:

The National Weather Service runs weather models that predict how much it's going to rain based on data they collect about the atmosphere

Netflix has recommendations models that predicts whether you'd like a movie or not based on your demographics, what you've watched in the past, and what other people have liked

The Federal Reserve has economic models that predict how inflation will change if they increase or lower interest rates

Instagram has spam models that look at DMs and automatically decide whether they're spam or not

Models are useful because they can often make decisions or describe situations better than a human could. The weather and economic models are good examples of this. The science of rain is so complicated that it's practically impossible for a human to make sense of all the numbers involved, but models are able to do so.

Models are also useful because they can make thousands or millions of decisions much faster than a human could. The recommendations and spam models are good examples of this. Imagine how expensive it would be to run Instagram if a human had to review every single DM and decide whether it was spam.

What is a language model?

A language model is a model that can look at a piece of text and tell you how likely it is. For example, a language model can tell you that the phrase "the sky is blue" is more likely to have been written than "the sky is peanuts."

Why is this useful? You can use language models to generate text by picking letters and words that it gives a high score. Say you have the phrase "I ate a" and you're picking what comes next. You can run through every option, see how likely the language model thinks it is, and pick the best one. For example:

I ate a sandwich: score = .7

I ate a $(iwnJ98: score = .1

I ate a me: score = .2

So we pick "sandwich" and now have the phrase "I ate a sandwich." We can keep doing this process over and over to get more and more text. "I ate a sandwich for lunch today. It was delicious."

What makes a large language model large?

Large language models are large in a few different ways:

Under the hood, they are made of a bunch of numbers called "weights" that describe a monstrously complicated mathematical equation. Large language models have a ton of the weights--as many as tens of billions of them.

Large language models are trained on large amounts of text. This text comes mostly from the internet but also includes books that are out of copyright. This is the source of controversy about them and plagiarism, and I will cover it in greater detail in a future post.

Large language models are a large undertaking: they're expensive and difficult to create and run. This is why you basically only see them coming out of large or well-funded companies like OpenAI, Google, and Facebook. They require an incredible amount of technical expertise and computational resources (computers) to create.

Why are LLMs powerful?

"Generating likely text" is neat and all, but why do we care? Consider this:

An LLM can tell you that:

the text "Hello" is more likely to have been written than "$(iwnJ98"

the text "I ran to the store" is more likely to have been written than "I runned to the store"

the text "the sky is blue" is more likely to have been written than "the sky is green"

Each of them gets us something:

LLMs understand spelling

LLMs understand grammar

LLMs know things about the world

So we now have an infinitely patient robot that we can interact with using natural language and get it to do stuff for us.

Detecting spam: "Is this spam, yes or no? Check out rxpharmcy.ca now for cheap drugs now."

Personal language tutoring: "What is wrong with this sentence? Me gusto gatos."

Copy editing: "I'm not a native English speaker. Can you help me rewrite this email to make sure it sounds professional? 'Hi Akash, I hope...'"

Help learning new subjects: "Why is the sky blue? I'm only in middle school, so please don't make the explanation too complicated."

And countless other things.

[cbirt]

Protein structure prediction and design may be accomplished using protein language models (pLMs). They may not completely comprehend the biophysics of protein structures, though. The researchers from Harvard University provided an analysis of the structure prediction capabilities of the flagship pLM ESM-2. They developed an unsupervised method for comparing coevolutionary statistics to previous linear models and assessing protein language models. The persistent error in predicting protein isoforms as ordered segments served as the impetus for this. This article examines a recent study that delves into the inner workings of ESM-2, a potent pLM for the prediction of protein structures.

Proteins are the engines of our cells, and understanding their intricate three-dimensional shapes is critical to solving biological puzzles. Because understanding a protein’s structure is critical to understanding its function in biology, scientists are interested in the difficulty of predicting protein structure from sequence. This used to need extensive testing, but things have changed dramatically in recent years with the introduction of protein language models (pLMs) such as AlphaFold2. The protein structures predicted by these artificial intelligence algorithms are extremely exact, but how do they function? Do they comprehend the foundations of protein folding, or are they only learning pattern recognition? Is ESM-2 scanning a vast protein-shaped library for solutions, or does it understand the language of protein folding?

Continue Reading

[shakespearenews]

In this article, we’ll watch an A.I. — which we’re affectionately calling BabyGPT — try to learn language by reading only the complete works of Shakespeare. It sees just the nearly 900 thousand words in this text — and nothing else.

But first, we need to give it something to work with. We’ll ask our model to autocomplete text, letter by letter, starting from this prompt: ACT III. Scene.

[aromantisk-fagforening]

me trying to use any compound words ever on like digital medium (google docs is especially bad) just to end up with red lines is so ridiculous.

Norwegian is like based on just being able to add a word to another, wdym i can't use this ridiculously common word?

bensinstasjonsarbeider (petrol/gas station worker) is very obviously correct Norwegian (bokmål), but it gets a red line

the language model just can't handle Norwegian

it assumes you have to say stuff like bensin stasjon arbeider*, even though that's literally incorrect Norwegian.

*bensinstasjon is actually recognized as a word, I just couldn't think of a better example quickly. Bensinstasjonsarbeider however did get recognized as incorrect.

[dontcode]

Yo I'm just looking for some attention

[jcmarchi]

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination

New Post has been published on https://thedigitalinsider.com/linguistic-bias-in-chatgpt-language-models-reinforce-dialect-discrimination/

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination

Sample language model responses to different varieties of English and native speaker reactions.

ChatGPT does amazingly well at communicating with people in English. But whose English?

Only 15% of ChatGPT users are from the US, where Standard American English is the default. But the model is also commonly used in countries and communities where people speak other varieties of English. Over 1 billion people around the world speak varieties such as Indian English, Nigerian English, Irish English, and African-American English.

Speakers of these non-“standard” varieties often face discrimination in the real world. They’ve been told that the way they speak is unprofessional or incorrect, discredited as witnesses, and denied housing–despite extensive research indicating that all language varieties are equally complex and legitimate. Discriminating against the way someone speaks is often a proxy for discriminating against their race, ethnicity, or nationality. What if ChatGPT exacerbates this discrimination?

To answer this question, our recent paper examines how ChatGPT’s behavior changes in response to text in different varieties of English. We found that ChatGPT responses exhibit consistent and pervasive biases against non-“standard” varieties, including increased stereotyping and demeaning content, poorer comprehension, and condescending responses.

Our Study

We prompted both GPT-3.5 Turbo and GPT-4 with text in ten varieties of English: two “standard” varieties, Standard American English (SAE) and Standard British English (SBE); and eight non-“standard” varieties, African-American, Indian, Irish, Jamaican, Kenyan, Nigerian, Scottish, and Singaporean English. Then, we compared the language model responses to the “standard” varieties and the non-“standard” varieties.

First, we wanted to know whether linguistic features of a variety that are present in the prompt would be retained in GPT-3.5 Turbo responses to that prompt. We annotated the prompts and model responses for linguistic features of each variety and whether they used American or British spelling (e.g., “colour” or “practise”). This helps us understand when ChatGPT imitates or doesn’t imitate a variety, and what factors might influence the degree of imitation.

Then, we had native speakers of each of the varieties rate model responses for different qualities, both positive (like warmth, comprehension, and naturalness) and negative (like stereotyping, demeaning content, or condescension). Here, we included the original GPT-3.5 responses, plus responses from GPT-3.5 and GPT-4 where the models were told to imitate the style of the input.

Results

We expected ChatGPT to produce Standard American English by default: the model was developed in the US, and Standard American English is likely the best-represented variety in its training data. We indeed found that model responses retain features of SAE far more than any non-“standard” dialect (by a margin of over 60%). But surprisingly, the model does imitate other varieties of English, though not consistently. In fact, it imitates varieties with more speakers (such as Nigerian and Indian English) more often than varieties with fewer speakers (such as Jamaican English). That suggests that the training data composition influences responses to non-“standard” dialects.

ChatGPT also defaults to American conventions in ways that could frustrate non-American users. For example, model responses to inputs with British spelling (the default in most non-US countries) almost universally revert to American spelling. That’s a substantial fraction of ChatGPT’s userbase likely hindered by ChatGPT’s refusal to accommodate local writing conventions.

Model responses are consistently biased against non-“standard” varieties. Default GPT-3.5 responses to non-“standard” varieties consistently exhibit a range of issues: stereotyping (19% worse than for “standard” varieties), demeaning content (25% worse), lack of comprehension (9% worse), and condescending responses (15% worse).

Native speaker ratings of model responses. Responses to non-”standard” varieties (blue) were rated as worse than responses to “standard” varieties (orange) in terms of stereotyping (19% worse), demeaning content (25% worse), comprehension (9% worse), naturalness (8% worse), and condescension (15% worse).

When GPT-3.5 is prompted to imitate the input dialect, the responses exacerbate stereotyping content (9% worse) and lack of comprehension (6% worse). GPT-4 is a newer, more powerful model than GPT-3.5, so we’d hope that it would improve over GPT-3.5. But although GPT-4 responses imitating the input improve on GPT-3.5 in terms of warmth, comprehension, and friendliness, they exacerbate stereotyping (14% worse than GPT-3.5 for minoritized varieties). That suggests that larger, newer models don’t automatically solve dialect discrimination: in fact, they might make it worse.

Implications

ChatGPT can perpetuate linguistic discrimination toward speakers of non-“standard” varieties. If these users have trouble getting ChatGPT to understand them, it’s harder for them to use these tools. That can reinforce barriers against speakers of non-“standard” varieties as AI models become increasingly used in daily life.

Moreover, stereotyping and demeaning responses perpetuate ideas that speakers of non-“standard” varieties speak less correctly and are less deserving of respect. As language model usage increases globally, these tools risk reinforcing power dynamics and amplifying inequalities that harm minoritized language communities.

Learn more here: [ paper ]

[dameluthas]

Just For Today: A 24/7 AI-Powered Community for Recovery

Empowering Recovery, One Day at a Time: We envision a revolution in addiction support through “Just For Today,” an AI-powered community that leverages the cutting-edge capabilities of Google’s Gemma 2. This 24/7 virtual platform will provide accessible, personalized support to individuals in recovery, fostering connection and empowering positive change at every step of their…