Uncovering ChatGPT Usage In Academic Papers Through Excess Vocabulary

Frequencies of PubMed abstracts containing certain words. Black lines show counterfactual extrapolations from 2021–22 to 2023–24. The first six words are affected by ChatGPT; the last three relate to major events that influenced scientific writing and are shown for comparison. (Credit: Kobak et al., 2024)
Frequencies of PubMed abstracts containing certain words. Black lines show counterfactual extrapolations from 2021–22 to 2023–24. The first six words are affected by ChatGPT; the last three relate to major events that influenced scientific writing and are shown for comparison. (Credit: Kobak et al., 2024)

That students these days love to use ChatGPT for assistance with reports and other writing tasks is hardly a secret, but in academics it’s becoming ever more prevalent as well. This raises the question of whether ChatGPT-assisted academic writings can be distinguished somehow. According to [Dmitry Kobak] and colleagues this is the case, with a strong sign of ChatGPT use being the presence of a lot of flowery excess vocabulary in the text. As detailed in their prepublication paper, the frequency of certain style words is a remarkable change in the used vocabulary of the published works examined.

For their study they looked at over 14 million biomedical abstracts from 2010 to 2024 obtained via PubMed. These abstracts were then analyzed for word usage and frequency, which shows both natural increases in word frequency (e.g. from the SARS-CoV-2 pandemic and Ebola outbreak), as well as massive spikes in excess vocabulary that coincide with the public availability of ChatGPT and similar LLM-based tools.

In total 774 unique excess words were annotated. Here ‘excess’ means ‘outside of the norm’, following the pattern of ‘excess mortality’ where mortality during one period noticeably deviates from patterns established during previous periods. In this regard the bump in words like respiratory are logical, but the surge in style words like intricate and notably would seem to be due to LLMs having a penchant for such flowery, overly dramatized language.

The researchers have made the analysis code available for those interested in giving it a try on another corpus. The main author also addressed the question of whether ChatGPT might be influencing people to write more like an LLM. At this point it’s still an open question of whether people would be more inclined to use ChatGPT-like vocabulary or actively seek to avoid sounding like an LLM.

McDonald’s Terminates Its Drive-Through Ordering AI Assistant

McDonald’s recently announced that it will be scrapping the voice-assistant which it has installed at over 100 of its drive-throughs after a two-year trial run. In the email that was sent to franchises, McDonald’s did say that they are still looking at voice ordering solutions for automated order taking (AOT), but it appears that for now the test was a disappointment. Judging by the many viral videos of customers struggling to place an order through the AOT system, it’s not hard to see why.

This AOT attempt began when in 2019 McDonald’s acquired AI company Apprente to create its McD Tech Labs, only to sell it again to IBM who then got contracted to create the technology for McDonald’s fast-food joints. When launched in 2021, it was expected that McDonald’s drive-through ordering lanes would eventually all be serviced by AOT, with an experience akin to the Alexa and Siri voice assistants that everyone knows and loves (to yell at).

With the demise of this test at McDonald’s, it would seem that the biggest change is likely to be in the wider automation of preparing fast-food instead, with robots doing the burger flipping and freedom frying rather than a human. That said, would you prefer the McD voice assistant when going through a Drive-Thru® over a human voice?

EMO: Alibaba’s Diffusion Model-Based Talking Portrait Generator

Alibaba’s EMO (or Emote Portrait Alive) framework is a recent entry in a series of attempts to generate a talking head using existing audio (spoken word or vocal audio) and a reference portrait image as inputs. At its core it uses a diffusion model that is trained on 250 hours of video footage and over 150 million images. But unlike previous attempts, it adds what the researchers call a speed controller and a face region controller. These serve to stabilize the generated frames, along with an additional module to stop the diffusion model from outputting frames that feature a result too distinct from the reference image used as input.

In the related paper by [Linrui Tian] and colleagues a number of comparisons are shown between EMO and other frameworks, claiming significant improvements over these. A number of examples of talking and singing heads generated using this framework are provided by the researchers, which gives some idea of what are probably the ‘best case’ outputs. With some examples, like [Leslie Cheung Kwok Wing] singing ‘Unconditional‘ big glitches are obvious and there’s a definite mismatch between the vocal track and facial motions. Despite this, it’s quite impressive, especially with fairly realistic movement of the head including blinking of the eyes.

Meanwhile some seem extremely impressed, such as in a recent video by [Matthew Berman] on EMO where he states that Alibaba releasing this framework to the public might be ‘too dangerous’. The level-headed folks over at PetaPixel however also note the obvious visual imperfections that are a dead give-away for this kind of generative technology. Much like other diffusion model-based generators, it would seem that EMO is still very much stuck in the uncanny valley, with no clear path to becoming a real human yet.

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What If The Matrix Was Made In The 1950s?

We’ve noticed a recent YouTube trend of producing trailers for shows and movies as if they were produced in the 1950s, even when they weren’t. The results are impressive and, as you might expect, leverage AI generation tools. While we enjoy watching them, we were especially interested in [Patrick Gibney’s] peek behind the curtain of how he makes them, as you can see below. If you want to see an example of the result first, check out the second video, showing a 1950s-era The Matrix.

Of course, you could do some of it yourself, but if you want the full AI experience, [Patrick] suggests using ChatGPT to produce a script, though he admits that if he did that, he would tweak the results. Other AI tools create the pictures used and the announcer-style narration. Another tool produces cinematographic shots that include the motion of the “actors” and other things in the scene. More tools create the background music.

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Can You Hear Me Now? Try These Headphones

When you are young, you take it for granted that you can pick out a voice in a crowded room or a factory floor. But as you get older, your hearing often gets to the point where a noisy room merges into a mishmash of sounds. University of Washington researchers have developed what they call Target Speech Hearing. In plain English, it is an AI-powered headphone that lets you look at someone and pull their voice out of the chatter. For best results, however, have to enroll their voice first, so it wouldn’t make a great eavesdropping device.

If you want to dive into the technical details, their paper goes into how it works. The prototype uses a Sony noise-cancelling headset. However, the system requires binaural microphones so additional microphones attach to the outside of the headphones.

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Feast Your Eyes On These AI-Generated Sounds

The radio hackers in the audience will be familiar with a spectrogram display, but for the uninitiated, it’s basically a visual representation of how a range of frequencies are changing with time. Usually such a display is used to identify a clear transmission in a sea of noise, but with the right software, it’s possible to generate a signal that shows up as text or an image when viewed as a spectrogram. Musicians even occasionally use the technique to hide images in their songs. Unfortunately, the audio side of such a trick generally sounds like gibberish to human ears.

Or at least, it used to. Students from the University of Michigan have found a way to use diffusion models to not only create a spectrogram image for a given prompt, but to do it with audio that actually makes sense given what the image shows. So for example if you asked for a spectrogram of a race car, you might get an audio track that sounds like a revving engine.

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Try Image Classification Running In Your Browser, Thanks To WebGPU

When something does zero-shot image classification, that means it’s able to make judgments about the contents of an image without the user needing to train the system beforehand on what to look for. Watch it in action with this online demo, which uses WebGPU to implement CLIP (Contrastive Language–Image Pre-training) running in one’s browser, using the input from an attached camera.

By giving the program some natural language visual concept labels (such as ‘person’ or ‘cat’) that fit a hypothetical template for the image content, the system will output — in real-time — its judgement on the appropriateness of such labels to what the camera sees. Again, all of this runs locally.

It’s maybe a little bit unintuitive, but what’s happening in the demo is that the system is deciding which of the user-provided labels (“a photo of a cat” vs “a photo of a bald man”, for example) is most appropriate to what the camera sees. The more a particular label is judged a good fit for the image, the higher the number beside it.

This kind of process benefits greatly from shoveling the hard parts of the computation onto compatible graphics cards, which is exactly what WebGPU provides by allowing the browser access to a local GPU. WebGPU is relatively recent, but we’ve already seen it used to run LLMs (Large Language Models) directly in the browser.

Wondering what makes GPUs so very useful for AI-type applications? It’s all about their ability to work with enormous amounts of data very quickly.