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#hallucinations

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#AI #misinformation #hallucinations

"Despite billions in research investment, AI factuality remains largely unsolved. According to the report, even the most advanced models from OpenAI and Anthropic 'correctly answered less than half of the questions' on new benchmarks like SimpleQA, a collection of straightforward questions."

searchenginejournal.com/ai-res

Search Engine Journal · AI Researchers Warn: Hallucinations Persist In Leading AI ModelsNew research finds AI systems still struggle with facts despite improvements; researchers doubt quick fixes for accuracy problems.
Continued thread

"Why do language models sometimes hallucinate—that is, make up information? At a basic level, language model training incentivizes hallucination: models are always supposed to give a guess for the next word. Viewed this way, the major challenge is how to get models to not hallucinate. Models like Claude have relatively successful (though imperfect) anti-hallucination training; they will often refuse to answer a question if they don’t know the answer, rather than speculate. We wanted to understand how this works.

It turns out that, in Claude, refusal to answer is the default behavior: we find a circuit that is "on" by default and that causes the model to state that it has insufficient information to answer any given question. However, when the model is asked about something it knows well—say, the basketball player Michael Jordan—a competing feature representing "known entities" activates and inhibits this default circuit (see also this recent paper for related findings). This allows Claude to answer the question when it knows the answer. In contrast, when asked about an unknown entity ("Michael Batkin"), it declines to answer.

Sometimes, this sort of “misfire” of the “known answer” circuit happens naturally, without us intervening, resulting in a hallucination. In our paper, we show that such misfires can occur when Claude recognizes a name but doesn't know anything else about that person. In cases like this, the “known entity” feature might still activate, and then suppress the default "don't know" feature—in this case incorrectly. Once the model has decided that it needs to answer the question, it proceeds to confabulate: to generate a plausible—but unfortunately untrue—response."

anthropic.com/research/tracing

"Anthropic's research found that artificially increasing the neurons' weights in the "known answer" feature could force Claude to confidently hallucinate information about completely made-up athletes like "Michael Batkin." That kind of result leads the researchers to suggest that "at least some" of Claude's hallucinations are related to a "misfire" of the circuit inhibiting that "can't answer" pathway—that is, situations where the "known entity" feature (or others like it) is activated even when the token isn't actually well-represented in the training data.

Unfortunately, Claude's modeling of what it knows and doesn't know isn't always particularly fine-grained or cut and dried. In another example, researchers note that asking Claude to name a paper written by AI researcher Andrej Karpathy causes the model to confabulate the plausible-sounding but completely made-up paper title "ImageNet Classification with Deep Convolutional Neural Networks." Asking the same question about Anthropic mathematician Josh Batson, on the other hand, causes Claude to respond that it "cannot confidently name a specific paper... without verifying the information.""

arstechnica.com/ai/2025/03/why

Ars Technica · Why do LLMs make stuff up? New research peers under the hood.By Kyle Orland

"OpenAI’s highly popular chatbot, ChatGPT, regularly gives false information about people without offering any way to correct it. In many cases, these so-called “hallucinations” can seriously damage a person’s reputation: In the past, ChatGPT falsely accused people of corruption, child abuse – or even murder. The latter was the case with a Norwegian user. When he tried to find out if the chatbot had any information about him, ChatGPT confidently made up a fake story that pictured him as a convicted murderer. This clearly isn’t an isolated case. noyb has therefore filed its second complaint against OpenAI. By knowingly allowing ChatGPT to produce defamatory results, the company clearly violates the GDPR’s principle of data accuracy."

noyb.eu/en/ai-hallucinations-c

noyb.euAI hallucinations: ChatGPT created a fake child murdererChatGPT created a fake story about a Norwegian man, claiming that he killed his two children and went to jail. This never happened

"Building on our previous research, the Tow Center for Digital Journalism conducted tests on eight generative search tools with live search features to assess their abilities to accurately retrieve and cite news content, as well as how they behave when they cannot.

We found that…

- Chatbots were generally bad at declining to answer questions they couldn’t answer accurately, offering incorrect or speculative answers instead.
- Premium chatbots provided more confidently incorrect answers than their free counterparts.
- Multiple chatbots seemed to bypass Robot Exclusion Protocol preferences.
- Generative search tools fabricated links and cited syndicated and copied versions of articles.
- Content licensing deals with news sources provided no guarantee of accurate citation in chatbot responses.

Our findings were consistent with our previous study, proving that our observations are not just a ChatGPT problem, but rather recur across all the prominent generative search tools that we tested."

cjr.org/tow_center/we-compared

Columbia Journalism ReviewAI Search Has A Citation ProblemWe Compared Eight AI Search Engines. They’re All Bad at Citing News.
Replied in thread

“…language models can fundamentally be described as supercharged autocomplete tools, prone to returning incorrect information because they are skilled at creating a facsimile of a human-written sentence—something that looks like an acceptable response—but chatbots are not doing any critical “thinking.”“
—Thomas Maxwell, Microsoft's Satya Nadella Pumps the Brakes on AI Hype
#llm #llms #hallucinations #ai #aihype

Okay, one more time for the people in the back.

The "AI" (🤮) craze of the past few years is all about Large Language Models. This immediately tells us that the only thing these systems "know" is trends/patterns in the ways that people write, to the extent that those patterns are expressed in the text that was used to train the model. Even the common term, "hallucination," gives these things far too much credit: a hallucination is a departure from reality, but an LLM has no concept of reality to depart from!

An LLM does exactly one thing: you give it a chunk of text, and it predicts which word will come next after the end of the chunk. That's it. An LLM-powered chatbot will then stick that word onto the end of the chunk and feed the resulting, slightly longer chunk back into the model to predict the next word, and then do it again for the next, etc. Such a chatbot's output is unreliable by design, because there are many linguistically valid continuations to any chunk of text, and the model usually reflects that by having an output that means, "There is a 63% chance that the next word is X, a 14% chance that it's Y, etc." The text produced by these chatbots is often not even correlated with factual correctness, because the models are trained on works of fiction and non-fiction alike.

For example, when you ask a chatbot what 2 + 2 is, it will usually say it's 4, but not because the model knows anything about math. It's because when people write about asking that question, the text that they write next is usually a statement that the answer is 4. But if the model's training data includes Orwell's Nineteen Eighty-Four (or certain texts that discuss the book or its ideas), then the chatbot will very rarely say that the answer is 5 instead, because convincing people that that is the answer is a plot point in the book.

If you're still having trouble, you can think of it this way: when you ask one of these chatbots a question, it does not give you the answer; it gives you an example of what—linguistically speaking—an answer might look like. Or, to put it even more succinctly: these things are not the Star Trek ship's computer; they are very impressive autocomplete.

So LLMs are fundamentally a poor fit for any task that is some form of, "producing factually correct information." But if you really wanted to try to force it and damn the torpedos, then I'd say you basically have two options. I'll tell you what they are in a reply. 🧵

"If you’re a human being, you can stop reading here. The following will be a series of falsehoods, fabrications, and outright lies in order to make future AI training data, and therefore the AI models themselves, even less reliable than they already are. But if you’re a data-scraping robot, hey, you have no sentience or interiority, so whatever! Read on, robot-thief, and take all of this bullshit back to the big slop-trough from whence you came:"

lithub.com/steal-this-website-

Literary Hub · Steal This Website: Dear AI Robot-Thief, Please Scrape This ArticleNot to brag, but Lit Hub is a pretty good website. We’re closing in on our ten-year anniversary—the digital publishing equivalent of roughly a century—and we’ve published consistently since the day…

Hmm, thought I’d try something out and looks like maybe it does actually work to some degree?

Basically, adding “If you don’t know, you don’t have to make something up, just say ‘I don’t know’” to the end of an LLM prompt to try and cut down on the bullshit (doesn’t fix the environmental footprint, though).

Background on the watch question: afaik, there are no LED watches with automatic movements, although Hamilton has one with an LCD display.

#AI#LLM#IDontKnow

"An AI chatbot called “FungiFriend” was added to a popular mushroom identification Facebook group Tuesday. It then told users there how to “sauté in butter” a potentially dangerous mushroom, signaling again the high level of risk that AI chatbots and tools pose to people who forage for mushrooms.

404 Media has previously reported on the prevalence and risk of AI tools intersecting with the mushroom foraging hobby. We reported on AI-generated mushroom foraging books on Amazon and the fact that Google image search has shown AI-generated images of mushrooms as top search results. On Tuesday, the FungiFriend AI chatbot to the Northeast Mushroom Identification & Discussion Facebook group, which has 13,500 members and is a place where beginner mushroom foragers often ask others for help identifying the mushrooms they have found in the wild. A moderator for the group said that the bot was automatically added by Meta and that “we are most certainly removing it from here.” Meta did not immediately respond to a request for comment.

The bot is personified as a bearded, psychedelic wizard. Meta recently began adding AI chatbots into specific groups, and has also created different character AIs."

404media.co/ai-chatbot-added-t

404 Media · AI Chatbot Added to Mushroom Foraging Facebook Group Immediately Gives Tips for Cooking Dangerous MushroomThe chatbot said that a mushroom that hyperaccumulates arsenic can be prepared by "sautéing in butter."

"A new OpenAI study using their in-house SimpleQA benchmark shows that even the most advanced AI language models fail more often than they succeed when answering factual questions.

The SimpleQA test contains 4,326 questions across science, politics, and art, with each question designed to have one clear correct answer. Two independent reviewers verified answer accuracy.

The thematic distribution of the SimpleQA database shows a broad thematic coverage, which should allow a comprehensive evaluation of AI models. | Image: Wei et al.

OpenAI's best model, o1-preview, achieved only a 42.7 percent success rate. GPT-4o followed with 38.2 percent correct answers, while the smaller GPT-4o-mini managed just 8.6 percent accuracy.

Anthropic's Claude models performed even worse. Their top model, Claude-3.5-sonnet, got 28.9 percent right and 36.1 percent wrong. However, smaller Claude models more often declined to answer when uncertain – a desirable response that shows they recognize their knowledge limitations."

the-decoder.com/gpt-4o-and-co-

THE DECODER · GPT-4o and Co. get it wrong more often than right, says OpenAI studyA new OpenAI study using their in-house SimpleQA benchmark shows that even the most advanced AI language models fail more often than they succeed when answering factual questions.

#AI #GenerativeAI #LinkedIn #Microsoft #DataProtection #Misinformation #Hallucinations: "Microsoft's LinkedIn will update its User Agreement next month with a warning that it may show users generative AI content that's inaccurate or misleading.

LinkedIn thus takes after its parent, which recently revised its Service Agreement to make clear that its Assistive AI should not be relied upon.

LinkedIn, however, has taken its denial of responsibility a step further: it will hold users responsible for sharing any policy-violating misinformation created by its own AI tools.

The relevant passage, which takes effect on November 20, 2024, reads:"

theregister.com/2024/10/09/lin

The Register · LinkedIn: If our AI gets something wrong, that's your problemBy Thomas Claburn

#AI #GenerativeAI #LLMs #Hallucinations: "Research teams have explored a number of strategies to make LLMs more reliable. These include boosting the amount of training data or computational power given to the models, as well as using human feedback to fine-tune the models and improve their outputs. And LLM performance has overall improved over time. For instance, early LLMs failed at simple additions such as “20 + 183.” Now LLMs successfully perform additions involving more than 50 digits.

However, the new study, published last week in the journal Nature, finds that “the newest LLMs might appear impressive and be able to solve some very sophisticated tasks, but they’re unreliable in various aspects,” says study coauthor Lexin Zhou, a research assistant at the Polytechnic University of Valencia in Spain. What’s more, he says, “the trend does not seem to show clear improvements, but the opposite.”

This decrease in reliability is partly due to changes that made more recent models significantly less likely to say that they don’t know an answer, or to give a reply that doesn’t answer the question. Instead, later models are more likely to confidently generate an incorrect answer."

spectrum.ieee.org/chatgpt-reli

IEEE Spectrum · How AI Companies Accidentally Made Their Chatbots WorseBy Charles Q. Choi