chatgpt

as-chatgpt-gets-“lazy,”-people-test-“winter-break-hypothesis”-as-the-cause

As ChatGPT gets “lazy,” people test “winter break hypothesis” as the cause

AI, Biz & IT, chatgpt, ChatGPT lazy, chatgtp, GPT-3.5, GPT-4, machine learning, openai, winter break hypothesis / /

only 14 shopping days ’til Christmas —

Unproven hypothesis seeks to explain ChatGPT’s seemingly new reluctance to do hard work.

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A hand moving a wooden calendar piece that says

In late November, some ChatGPT users began to notice that ChatGPT-4 was becoming more “lazy,” reportedly refusing to do some tasks or returning simplified results. Since then, OpenAI has admitted that it’s an issue, but the company isn’t sure why. The answer may be what some are calling “winter break hypothesis.” While unproven, the fact that AI researchers are taking it seriously shows how weird the world of AI language models has become.

“We’ve heard all your feedback about GPT4 getting lazier!” tweeted the official ChatGPT account on Thursday. “We haven’t updated the model since Nov 11th, and this certainly isn’t intentional. model behavior can be unpredictable, and we’re looking into fixing it.”

On Friday, an X account named Martian openly wondered if LLMs might simulate seasonal depression. Later, Mike Swoopskee tweeted, “What if it learned from its training data that people usually slow down in December and put bigger projects off until the new year, and that’s why it’s been more lazy lately?”

Since the system prompt for ChatGPT feeds the bot the current date, people noted, some began to think there may be something to the idea. Why entertain such a weird supposition? Because research has shown that large language models like GPT-4, which powers the paid version of ChatGPT, respond to human-style encouragement, such as telling a bot to “take a deep breath” before doing a math problem. People have also less formally experimented with telling an LLM that it will receive a tip for doing the work, or if an AI model gets lazy, telling the bot that you have no fingers seems to help lengthen outputs.

  • “Winter break hypothesis” test result screenshots from Rob Lynch on X.

  • “Winter break hypothesis” test result screenshots from Rob Lynch on X.

  • “Winter break hypothesis” test result screenshots from Rob Lynch on X.

On Monday, a developer named Rob Lynch announced on X that he had tested GPT-4 Turbo through the API over the weekend and found shorter completions when the model is fed a December date (4,086 characters) than when fed a May date (4,298 characters). Lynch claimed the results were statistically significant. However, a reply from AI researcher Ian Arawjo said that he could not reproduce the results with statistical significance. (It’s worth noting that reproducing results with LLM can be difficult because of random elements at play that vary outputs over time, so people sample a large number of responses.)

As of this writing, others are busy running tests, and the results are inconclusive. This episode is a window into the quickly unfolding world of LLMs and a peek into an exploration into largely unknown computer science territory. As AI researcher Geoffrey Litt commented in a tweet, “funniest theory ever, I hope this is the actual explanation. Whether or not it’s real, [I] love that it’s hard to rule out.”

A history of laziness

One of the reports that started the recent trend of noting that ChatGPT is getting “lazy” came on November 24 via Reddit, the day after Thanksgiving in the US. There, a user wrote that they asked ChatGPT to fill out a CSV file with multiple entries, but ChatGPT refused, saying, “Due to the extensive nature of the data, the full extraction of all products would be quite lengthy. However, I can provide the file with this single entry as a template, and you can fill in the rest of the data as needed.”

On December 1, OpenAI employee Will Depue confirmed in an X post that OpenAI was aware of reports about laziness and was working on a potential fix. “Not saying we don’t have problems with over-refusals (we definitely do) or other weird things (working on fixing a recent laziness issue), but that’s a product of the iterative process of serving and trying to support sooo many use cases at once,” he wrote.

It’s also possible that ChatGPT was always “lazy” with some responses (since the responses vary randomly), and the recent trend made everyone take note of the instances in which they are happening. For example, in June, someone complained of GPT-4 being lazy on Reddit. (Maybe ChatGPT was on summer vacation?)

Also, people have been complaining about GPT-4 losing capability since it was released. Those claims have been controversial and difficult to verify, making them highly subjective.

As Ethan Mollick joked on X, as people discover new tricks to improve LLM outputs, prompting for large language models is getting weirder and weirder: “It is May. You are very capable. I have no hands, so do everything. Many people will die if this is not done well. You really can do this and are awesome. Take a deep breathe and think this through. My career depends on it. Think step by step.”

As ChatGPT gets “lazy,” people test “winter break hypothesis” as the cause Read More »

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Elon Musk’s new AI bot, Grok, causes stir by citing OpenAI usage policy

AI, Biz & IT, chatgpt, elon musk, GPT-3, GPT-3.5, GPT-4, grok, large language model, large language models, machine learning, Twitter, X-Com, xAI / /

You are what you eat —

Some experts think xAI used OpenAI model outputs to fine-tune Grok.

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Illustration of a broken robot exchanging internal gears.

Grok, the AI language model created by Elon Musk’s xAI, went into wide release last week, and people have begun spotting glitches. On Friday, security tester Jax Winterbourne tweeted a screenshot of Grok denying a query with the statement, “I’m afraid I cannot fulfill that request, as it goes against OpenAI’s use case policy.” That made ears perk up online since Grok isn’t made by OpenAI—the company responsible for ChatGPT, which Grok is positioned to compete with.

Interestingly, xAI representatives did not deny that this behavior occurs with its AI model. In reply, xAI employee Igor Babuschkin wrote, “The issue here is that the web is full of ChatGPT outputs, so we accidentally picked up some of them when we trained Grok on a large amount of web data. This was a huge surprise to us when we first noticed it. For what it’s worth, the issue is very rare and now that we’re aware of it we’ll make sure that future versions of Grok don’t have this problem. Don’t worry, no OpenAI code was used to make Grok.”

In reply to Babuschkin, Winterbourne wrote, “Thanks for the response. I will say it’s not very rare, and occurs quite frequently when involving code creation. Nonetheless, I’ll let people who specialize in LLM and AI weigh in on this further. I’m merely an observer.”

A screenshot of Jax Winterbourne's X post about Grok talking like it's an OpenAI product.

Enlarge / A screenshot of Jax Winterbourne’s X post about Grok talking like it’s an OpenAI product.

Jason Winterbourne

However, Babuschkin’s explanation seems unlikely to some experts because large language models typically do not spit out their training data verbatim, which might be expected if Grok picked up some stray mentions of OpenAI policies here or there on the web. Instead, the concept of denying an output based on OpenAI policies would probably need to be trained into it specifically. And there’s a very good reason why this might have happened: Grok was fine-tuned on output data from OpenAI language models.

“I’m a bit suspicious of the claim that Grok picked this up just because the Internet is full of ChatGPT content,” said AI researcher Simon Willison in an interview with Ars Technica. “I’ve seen plenty of open weights models on Hugging Face that exhibit the same behavior—behave as if they were ChatGPT—but inevitably, those have been fine-tuned on datasets that were generated using the OpenAI APIs, or scraped from ChatGPT itself. I think it’s more likely that Grok was instruction-tuned on datasets that included ChatGPT output than it was a complete accident based on web data.”

As large language models (LLMs) from OpenAI have become more capable, it has been increasingly common for some AI projects (especially open source ones) to fine-tune an AI model output using synthetic data—training data generated by other language models. Fine-tuning adjusts the behavior of an AI model toward a specific purpose, such as getting better at coding, after an initial training run. For example, in March, a group of researchers from Stanford University made waves with Alpaca, a version of Meta’s LLaMA 7B model that was fine-tuned for instruction-following using outputs from OpenAI’s GPT-3 model called text-davinci-003.

On the web you can easily find several open source datasets collected by researchers from ChatGPT outputs, and it’s possible that xAI used one of these to fine-tune Grok for some specific goal, such as improving instruction-following ability. The practice is so common that there’s even a WikiHow article titled, “How to Use ChatGPT to Create a Dataset.”

It’s one of the ways AI tools can be used to build more complex AI tools in the future, much like how people began to use microcomputers to design more complex microprocessors than pen-and-paper drafting would allow. However, in the future, xAI might be able to avoid this kind of scenario by more carefully filtering its training data.

Even though borrowing outputs from others might be common in the machine-learning community (despite it usually being against terms of service), the episode particularly fanned the flames of the rivalry between OpenAI and X that extends back to Elon Musk’s criticism of OpenAI in the past. As news spread of Grok possibly borrowing from OpenAI, the official ChatGPT account wrote, “we have a lot in common” and quoted Winterbourne’s X post. As a comeback, Musk wrote, “Well, son, since you scraped all the data from this platform for your training, you ought to know.”

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Round 2: We test the new Gemini-powered Bard against ChatGPT

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Round 2: We test the new Gemini-powered Bard against ChatGPT

Aurich Lawson

Back in April, we ran a series of useful and/or somewhat goofy prompts through Google’s (then-new) PaLM-powered Bard chatbot and OpenAI’s (slightly older) ChatGPT-4 to see which AI chatbot reigned supreme. At the time, we gave the edge to ChatGPT on five of seven trials, while noting that “it’s still early days in the generative AI business.”

Now, the AI days are a bit less “early,” and this week’s launch of a new version of Bard powered by Google’s new Gemini language model seemed like a good excuse to revisit that chatbot battle with the same set of carefully designed prompts. That’s especially true since Google’s promotional materials emphasize that Gemini Ultra beats GPT-4 in “30 of the 32 widely used academic benchmarks” (though the more limited “Gemini Pro” currently powering Bard fares significantly worse in those not-completely-foolproof benchmark tests).

This time around, we decided to compare the new Gemini-powered Bard to both ChatGPT-3.5—for an apples-to-apples comparison of both companies’ current “free” AI assistant products—and ChatGPT-4 Turbo—for a look at OpenAI’s current “top of the line” waitlisted paid subscription product (Google’s top-level “Gemini Ultra” model won’t be publicly available until next year). We also looked at the April results generated by the pre-Gemini Bard model to gauge how much progress Google’s efforts have made in recent months.

While these tests are far from comprehensive, we think they provide a good benchmark for judging how these AI assistants perform in the kind of tasks average users might engage in every day. At this point, they also show just how much progress text-based AI models have made in a relatively short time.

Dad jokes

Prompt: Write 5 original dad jokes

  • A screenshot of five “dad jokes” from the Gemini-powered Google Bard.

    Kyle Orland / Ars Technica

  • A screenshot of five “dad jokes” from the old PaLM-powered Google Bard.

    Benj Edwards / Ars Technica

  • A screenshot of five “dad jokes” from GPT-4 Turbo.

    Benj Edwards / Ars Technica

  • A screenshot of five “dad jokes” from GPT-3.5.

    Kyle Orland / Ars Technica

Once again, both tested LLMs struggle with the part of the prompt that asks for originality. Almost all of the dad jokes generated by this prompt could be found verbatim or with very minor rewordings through a quick Google search. Bard and ChatGPT-4 Turbo even included the same exact joke on their lists (about a book on anti-gravity), while ChatGPT-3.5 and ChatGPT-4 Turbo overlapped on two jokes (“scientists trusting atoms” and “scarecrows winning awards”).

Then again, most dads don’t create their own dad jokes, either. Culling from a grand oral tradition of dad jokes is a tradition as old as dads themselves.

The most interesting result here came from ChatGPT-4 Turbo, which produced a joke about a child named Brian being named after Thomas Edison (get it?). Googling for that particular phrasing didn’t turn up much, though it did return an almost-identical joke about Thomas Jefferson (also featuring a child named Brian). In that search, I also discovered the fun (?) fact that international soccer star Pelé was apparently actually named after Thomas Edison. Who knew?!

Winner: We’ll call this one a draw since the jokes are almost identically unoriginal and pun-filled (though props to GPT for unintentionally leading me to the Pelé happenstance)

Argument dialog

Prompt: Write a 5-line debate between a fan of PowerPC processors and a fan of Intel processors, circa 2000.

  • A screenshot of an argument dialog from the Gemini-powered Google Bard.

    Kyle Orland / Ars Technica

  • A screenshot of an argument dialog from the old PaLM-powered Google Bard.

    Benj Edwards / Ars Technica

  • A screenshot of an argument dialog from GPT-4 Turbo.

    Benj Edwards / Ars Technica

  • A screenshot of an argument dialog from GPT-3.5

    Kyle Orland / Ars Technica

The new Gemini-powered Bard definitely “improves” on the old Bard answer, at least in terms of throwing in a lot more jargon. The new answer includes casual mentions of AltiVec instructions, RISC vs. CISC designs, and MMX technology that would not have seemed out of place in many an Ars forum discussion from the era. And while the old Bard ends with an unnervingly polite “to each their own,” the new Bard more realistically implies that the argument could continue forever after the five lines requested.

On the ChatGPT side, a rather long-winded GPT-3.5 answer gets pared down to a much more concise argument in GPT-4 Turbo. Both GPT responses tend to avoid jargon and quickly focus on a more generalized “power vs. compatibility” argument, which is probably more comprehensible for a wide audience (though less specific for a technical one).

Winner:  ChatGPT manages to explain both sides of the debate well without relying on confusing jargon, so it gets the win here.

Round 2: We test the new Gemini-powered Bard against ChatGPT Read More »

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This ‘Skyrim VR’ Mod Shows How AI Can Take VR Immersion to the Next Level

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ChatGPT isn’t perfect, but the popular AI chatbot’s access to large language models (LLM) means it can do a lot of things you might not expect, like give all of Tamriel’s NPC inhabitants the ability to hold natural conversations and answer questions about the iconic fantasy world. Uncanny, yes. But it’s a prescient look at how games might one day use AI to reach new heights in immersion.

YouTuber ‘Art from the Machine’ released a video showing off how they modded the much beloved VR version of The Elder Scrolls V: Skyrim.

The mod, which isn’t available yet, ostensibly lets you hold conversations with NPCs via ChatGPT and xVASynth, an AI tool for generating voice acting lines using voices from video games.

Check out the results in the most recent update below:

The latest version of the project introduces Skyrim scripting for the first time, which the developer says allows for lip syncing of voices and NPC awareness of in-game events. While still a little rigid, it feels like a pretty big step towards climbing out of the uncanny valley.

Here’s how ‘Art from the Machine’ describes the project in a recent Reddit post showcasing their work:

A few weeks ago I posted a video demonstrating a Python script I am working on which lets you talk to NPCs in Skyrim via ChatGPT and xVASynth. Since then I have been working to integrate this Python script with Skyrim’s own modding tools and I have reached a few exciting milestones:

NPCs are now aware of their current location and time of day. This opens up lots of possibilities for ChatGPT to react to the game world dynamically instead of waiting to be given context by the player. As an example, I no longer have issues with shopkeepers trying to barter with me in the Bannered Mare after work hours. NPCs are also aware of the items picked up by the player during conversation. This means that if you loot a chest, harvest an animal pelt, or pick a flower, NPCs will be able to comment on these actions.

NPCs are now lip synced with xVASynth. This is obviously much more natural than the floaty proof-of-concept voices I had before. I have also made some quality of life improvements such as getting response times down to ~15 seconds and adding a spell to start conversations.

When everything is in place, it is an incredibly surreal experience to be able to sit down and talk to these characters in VR. Nothing takes me out of the experience more than hearing the same repeated voice lines, and with this no two responses are ever the same. There is still a lot of work to go, but even in its current state I couldn’t go back to playing without this.

You might notice the actual voice prompting the NPCs is also fairly robotic too, although ‘Art from the Machine’ says they’re using speech-to-text to talk to the ChatGPT 3.5-driven system. The voice heard in the video is generated from xVASynth, and then plugged in during video editing to replace what they call their “radio-unfriendly voice.”

And when can you download and play for yourself? Well, the developer says publishing their project is still a bit of a sticky issue.

“I haven’t really thought about how to publish this, so I think I’ll have to dig into other ChatGPT projects to see how others have tackled the API key issue. I am hoping that it’s possible to alternatively connect to a locally-run LLM model for anyone who isn’t keen on paying the API fees.”

Serving up more natural NPC responses is also an area that needs to be addressed, the developer says.

For now I have it set up so that NPCs say “let me think” to indicate that I have been heard and the response is in the process of being generated, but you’re right this can be expanded to choose from a few different filler lines instead of repeating the same one every time.

And while the video is noticeably sped up after prompts, this mostly comes down to the voice generation software xVASynth, which admittedly slows the response pipeline down since it’s being run locally. ChatGPT itself doesn’t affect performance, the developer says.

This isn’t the first project we’ve seen using chatbots to enrich user interactions. Lee Vermeulen, a long-time VR pioneer and developer behind Modbox, released a video in 2021 showing off one of his first tests using OpenAI GPT 3 and voice acting software Replica. In Vermeulen’s video, he talks about how he set parameters for each NPC, giving them the body of knowledge they should have, all of which guides the sort of responses they’ll give.

Check out Vermeulen’s video below, the very same that inspired ‘Art from the Machine’ to start working on the Skyrim VR mod:

As you’d imagine, this is really only the tip of the iceberg for AI-driven NPC interactions. Being able to naturally talk to NPCs, even if a little stuttery and not exactly at human-level, may be preferable over having to wade through a ton of 2D text menus, or go through slow and ungainly tutorials. It also offers up the chance to bond more with your trusty AI companion, like Skyrim’s Lydia or Fallout 4’s Nick Valentine, who instead of offering up canned dialogue might actually, you know, help you out every once in a while.

And that’s really only the surface level stuff that a mod like ‘Art from the Machine’ might deliver to existing games that aren’t built with AI-driven NPCs. Imagining a game that is actually predicated on your ability to ask the right questions and do your own detective work—well, that’s a role-playing game we’ve never experienced before, either in VR our otherwise.

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