8 ms·
Spotting LLMs with Binoculars: Zero-Shot Detection of Machine-Generated Text
- mazingo 3y ago[dead]
- ISL 3y agoIs a layman's interpretation of this to state: LLMs tend to perform like aggregated humanity, but any given human will differ. Since all the volume of a high-dimensional sphere is at the edge, almost nobody is like the mean, so the false-positive-rate is low? It's a clever plan, until the LLMs do some adversarial training....
- adamgordonbell 3y agoThis is a super clear explanation! Perhaps this measurement approximates a human reaction to chatGPT: 'This writing is distinctly indistinct.'
- actionfromafar 3y agoMade me think of how the bomber planes pilot seats were designed to the average human, which meant it fit no human.
- sdsaga12 3y agoSounds like the bed of Procrustes: https://en.wikipedia.org/wiki/Procrustes#Mythology https://en.wikipedia.org/wiki/Procrustes#Mythology
- __loam 3y agoYeah it kind of sucks for people who don't like these systems that efforts to resist them are essentially the same as using GANs to train them.
- shagie 3y agohttps://www.smithsonianmag.com/history/what-the-luddites-really-fought-against-264412/ https://www.smithsonianmag.com/history/what-the-luddites-rea... > They did not invent a machine to destroy technology, but they knew how to use one. In Yorkshire, they attacked frames with massive sledgehammers they called “Great Enoch,” after a local blacksmith who had manufactured both the hammers and many of the machines they intended to destroy. “Enoch made them,” they declared, “Enoch shall break them.” ... And another reference for the phrase... https://www.nigeltyas.co.uk/nigel-tyas-news/post/enoch-the-power-hammer.html https://www.nigeltyas.co.uk/nigel-tyas-news/post/enoch-the-p... > And here’s the funny thing. The weapons they reached for to wield and smash the machines were sledge hammers made by ... the Taylor brothers of Marsden. This irony was not lost on the Luddites and as they swung ‘Enoch’s hammers’ to damage his hated machines they cried: “Enoch made them, and Enoch shall break them”.
- HKH2 3y agoFighting fire with fire.
- lawlessone 3y ago>It's a clever plan, until the LLMs do some adversarial training.... it's an unwinnable war.
- skybrian 3y agoIt depends on what you're trying to achieve. For a spam filter that downranks low-effort noise (or equivalently, ranks more interesting stuff higher), it might be useful enough?
- WhitneyLand 3y agoYes, and do we even need to wait? You could ask GPT-4 right now to write something imperfect. Maybe optimizing for “an amount of imperfections, variability, and subtle but tell-tale clues as to convince an examiner that the writer is human and not a language model.”? If that’s not enough it could also be asked to read the paper itself and suggest additional countermeasures based on a technical review.
- maxglute 3y agoThis is very succinct, but can someone reduce more to explain it like i'm five level.
- SubiculumCode 3y agohttps://readingbyeugene.com/2016/05/15/there-is-no-such-thing-as-average-body-size/ https://readingbyeugene.com/2016/05/15/there-is-no-such-thin...
- isaacfung 3y agoAre You Normal? Hint: No https://allendowney.github.io/ProbablyOverthinkingIt/gaussian.html https://allendowney.github.io/ProbablyOverthinkingIt/gaussia...
- thomasahle 3y agoGood summary. I wonder if the "perform like aggregated humanity" behavior comes from using greedy decoding. If you actually sample according to the token probs, it seems like you should still end up "on the edge".
- jjackson5324 3y ago> false positive rate of 0.01% What would be an acceptable false positive rate for something like this to be used at schools and universities? Like, obviously 0.01% is not acceptable, but what would be?
- Etheryte 3y agoWhy do you think this rate is not acceptable? I would say it's more than acceptable, even as a single data point. If someone's submission comes up as positive on two separate occasions you've pretty much eliminated the chance of a false positive.
- declaredapple 3y agoI would agree with that it's usable as a single datapoint, used with others (other submissions, general performance, etc). However given that we already have professors literally failing people by just pasting and asking chatgpt, I'm not sure I'm comfortable with that.
- FLT8 3y agoUnless that person's writing style just happens to be very close to how the average human writes? If it's 0.01% for "any given human", I wonder what the numbers would look like for a human closer to average than usual?
- wrs 3y agoOnly if the measures for an individual are uncorrelated, which there’s no reason to assume. It needs to be <= 0.01% false positive for each individual author. If it’s just that 0.01% of all tests are false positive, that leaves the possibility than a given individual might have anywhere up to 100% false positives.
- Lerc 3y agoI feel like 0.01% is one of the more dangerous levels of false positives. Widespread use would result in far more than 10,000 tests resulting in the near certainty that innocent people would be accused. The moderately low false positive rate would then be leaned on to imply guilt. You might find for every 10,000 tests you get around 151 targets. For each one individually you can say they probably did it, but cumulatively you can say that one of them is probably innocent. Consider using two positives. Do schools and universities generate 100,000,000 essays per year? Sure would suck for that innocent person tagged as having a one in a hundred million chance of not being guilty.
- vicgalle_ 3y ago> https://twitter.com/minimaxir/status/1749893683137454194 https://twitter.com/minimaxir/status/1749893683137454194 Not very promising, though.
- yreg 3y agoWell that is not a novel text, is it?
- comex 3y agoThe paper itself goes into detail about why the US Constitution and other memorized texts are misclassified. It’s surprising but not a killer flaw, since in most contexts it would only apply to direct quotes of famous texts.
- deleted 3y ago[deleted]
- GuB-42 3y agoIt is what you get when you ask ChatGPT for the US constitution, so yeah, AI generated. This effect is described in the article. And depending on the context, it can be a feature rather than a bug. If you are using an LLM detector to check if a news article or student essay is "legit", then not only you don't want something from a LLM, but you don't want copy-paste plagiarism either. So for the purpose of checking legitimacy of supposedly original work, then it is a desirable kind of false positive. I suppose simpler techniques can then be use to check for verbatim copies of famous text.
- minimaxir 3y agoI just added another tweet with the caveat others mentioned. (It was not an intentional omission) It’s still not a nuance that most people trying to identify AI will respect even if they know it. Given that constraint, I really doubt the accuracy metrics as well.
- TuringNYC 3y agoThe false positive rate would kill most use cases here. Even 1/10000 false accusations of academic integrity would be too much.
- berkes 3y agoIs it bad if it's an accusation? Wouldn't it need additional data, such as actual proof, to become an allegation or even a claim or charge?
- TuringNYC 3y ago>> Wouldn't it need additional data, such as actual proof, to become an allegation or even a claim or charge? What the computer surfaces as an accusation becomes fact by the users of the computer. Even when the consequences are serious, this happens, an example from yesterday: https://www.nbcnews.com/news/us-news/man-says-ai-facial-recognition-software-falsely-idd-robbing-sunglass-h-rcna135627 https://www.nbcnews.com/news/us-news/man-says-ai-facial-reco... Why should we assume this would go any differently when the consequences are not as serious?
- lawlessone 3y ago>Wouldn't it need additional data, such as actual proof, to become an allegation or even a claim or charge? Yes but that hasn't stopped people before.
- zarzavat 3y agoJudging by the number of people who seem to blindly trust what ChatGPT says, yes it’s bad. A false positive rate of 1/10000 would be almost the worst case in fact: truthy enough that people believe that it works, while still creating a vast absolute number of false positives.
- JoeJonathan 3y agoDo you really think professors flag plagiarism only when it's cut and dry? Absolutely not. Plenty, if not most, flagged cases of plagiarism are ambiguous. The process at most colleges and universities typically accounts for this via something like review by an academic integrity committee.
- binsquare 3y agoI'm not convinced that we're on the right path in detecting ai generated content. We've been looking at the end result and making conclusions about the journey - and that will always comes with degrees of uncertainty. A false positive rate of 0.01% now probably will not be applicable as people adapt and grow alongside ai content. I wonder if anyone's working on software that documents the journey of the output similar to like git commits, such that we can analyze both the metadata (journey) & output (end result) to determine human authenticity.
- tomaskafka 3y agoEdit history is an awesome learning data (as they show the train of thought), but I can imagine that models will easily learn to generate it.
- vidarh 3y agoI write start to finish, and rarely edit. I used to write essays in pen during exams and hand in the first version. My teachers despaired, and I countered that my grades were far above average, so why would I do it differently? If you use edit history, you'll get lots of false positives.
- ramoz 3y agoMy team is trying to get to that, albeit are very early and hardly integrated enough into training processes/scaled-inference https://huggingface.co/spaces/EQTYLab/lineage-explorer https://huggingface.co/spaces/EQTYLab/lineage-explorer
- arxiv_papers 3y ago[dead]
- Imnimo 3y agoAccording to their demo, their Limitations section of their github repo is AI-generated. >All AI-generated text detectors aim for accuracy, but none are perfect and can have multiple failure modes (e.g., Binoculars is more proficient in detecting English language text compared to other languages). This implementation is for academic purposes only and should not be considered as a consumer product. We also strongly caution against using Binoculars (or any detector) without human supervision.
- etwigg 3y agoIf the text is good, and someday it will be, I don't care if an LLM wrote it. If it's bad, I don't care if a person wrote it. The only reason to care is that the implicit proof-of-work signal has broken because LLM text is so cheap. Open forums might need to be pay-per-submission someday...
- Jedd 3y agoThe problem as I see it is that 'text is good' has two distinct meanings - first, it doesn't sound like an AI wrote it, it's interesting, entertaining, in word 'readable'. The second is that it's accurate / true. It feels like we're happy to take the first as a surrogate for the second, or at least being good at the first drops our guard on questioning the second.
- neilv 3y agoIf you need to understand what a particular person is thinking, then text they wrote themself can tell you a lot about that.
- jameshart 3y agoExactly. We are at frame 3 of https://xkcd.com/810/ https://xkcd.com/810/: "But what will you do when spammers train their bots to make automated constructive and helpful comments?" We don't need ML-generated-text detectors. We need BS detectors. If they have false positives and trigger on human-generated BS, that's just a great side-benefit. "This comment could have been written by an AI" is good enough reason to exclude it. That which does not need to be said, need not be said. As that XKCD's title text says: "And what about all the people who won't be able to join the community because they're terrible at making helpful and constructive co-- ... oh."
- layer8 3y agoIf you only care about the text, maybe. But you may also care about the person who claims to have written the text. That being said, automatic detection seems like a lost cause.
- isaacfung 3y ago
- Jedd 3y agohttps://huggingface.co/spaces/tomg-group-umd/Binoculars https://huggingface.co/spaces/tomg-group-umd/Binoculars
- kristopolous 3y agoThis was an open kaggle prize for a while
- dr_dshiv 3y ago1. How well can it detect if the writer edits it? 2. How well can it detect if the prompter tries to hide it? 3. How well can it detect if people tend to start writing like chatGPT? I grade a lot of papers and encourage/teach chatGPT use. It is so easy for me to detect poor usage. Quality is still easy to distinguish. Skillful use of these tools is a meaningful skill. In fact, it usually requires the same underlying skill! (Close reading, purposefulness, authenticity, etc) I love chatGPT because it obviates stuffy academic writing. Who needs it. Be clear and direct, that’s valuable!
- cush 3y agoExactly. Perfect grammar, syntax, and essay structure can still yield an unconvincing argument.
- HKH2 3y agoNext we'll have editors that can tell us that what we're writing is trite.
- wolverine876 3y ago> I love chatGPT because it obviates stuffy academic writing. Who needs it. Be clear and direct, that’s valuable! The goal of a paper, I thought, is thinking and not writing. Without outside help (including the AI), clear and direct thinking is what leads to clear and direct writing. What is being achieved here?
- dr_dshiv 3y agoDo you use chatGPT? It’s a dialectic. You think with it. You have to tell it what you want, clearly and directly. It’s why it’s often not a time saver for writing once you know what you want. But it can help you get going when you don’t. Many other benefits. It does not guarantee good writing, far from it!
- wolverine876 3y ago
- ff317 3y agoMaybe rather than focusing so much on how to detect AI-generated content, we should instead focus on our general ability to validate the truthiness of content regardless of source. I don't really care if an AI wrote it, so long as the content is meaningful and informative. I do care if it's a load of junk, even if a human did write it.
- ttpphd 3y agoThere is no algorithm for truth.
- HeatrayEnjoyer 3y agoNo, but there are technical mechanisms that provide a high level of assurance in most scenarios. There's a reason iMessage does not suffer spam and why cheating in Xbox online gaming is non-existent.
- HKH2 3y agoTreating what your opponents consider truth as hostile is basically presuming they are arguing in bad faith. They may well be, but you may be misinformed. At any rate, iMessage does not verify that what is said is true. It just weeds out obviously hostile communication.
- HeatrayEnjoyer 3y agoI didn't say anything about truth, I'm speaking about a humanness signal.
- HKH2 3y agoTruth rests on axioms. You would have to teach an AI what you think is true, but then it would have a kind of confirmation bias.
- aaroninsf 3y agoSo much time and effort being wasted on a problem we are entirely unequipped to ever "solve." I guess the full-employment economy demands much of us.
- adaboese 3y agoI can talk a lot about this, since this is the space I've spent a lot in experimenting. All I will say is that all these detectors (a) create a ton of false-positives, and (b) are incredibly easy to bypass if you know what you are doing. As an example, one method that I found that works extremely well is to simply rewrite the article section by section with instructions that require to mimic the writing style of an arbitrary block of human written text. This works a lot better than (as an example) asking to write in a specific style. Like, if I just say something along the lines of "write in a casual style that conveys lightheartedness towards the topic" is not going to work as good as simply saying "rewrite mimicking the style in which the following text block is written X" (where X is an example of a block of human written text). There are some silly things that will (a) trigger human written text to be detected as a AI and (b) that allow to avoid AI detection, e.g. using broad dictionary tends to trigger AI bots to detect the text as written by AI. So if you are using Grammarly to "improve your writing", then don't be surprised if it gets flagged. The inverse is true too. If you some statistical analyzes to replace less common expressions with more common expressions, AI-text is less likely to be detected as AI. If someone is interested, I can talk a lot more about hundreds of experiments I've done by now.
- timinou 3y agoI am curious actually! In general about your experiments, but also about integrating this detection algorithm to wider systems. Did you run any autogpt-like experiments with the AI generated text as a critique? My use case is a bit different (decision-making), so I play with relative plausibility instead of writing style. But I haven't found convincing ways of "converging" quite yet, i.e. benchmarks that don't rely solely on LLMs themselves to give their output.
- adaboese 3y agoTo clarify, the style experiment I've referenced earlier was just that – an experiment. I did not implement those methods into my software. Instead, I focused on how to eliminate things like 'talking with authority without evidence', 'contradictions', 'talking in extremely abstract concepts', 'conclusions without insights', etc. If you need a dataset to benchmark against, download any articles from pre 2017. There are a few ready-made datasets floating around the Internet.
- vunderba 3y agoI have thousands of short hand style notations that I've written over the years. Recently, I've been having GPT rewrite them. I first provide GPT with approximately 8 kB worth of my longform writing, and then ask it to rewrite the short hand using similar diction and style. Concerned about this issue I would also run the corresponding outputs through any LLM detection programs I could find (ZeroGPT, etc). None of the outputs have ever been detected as being machine generated.
- stainablesteel 3y agoim amazed at how difficult this has proven to be every time i ask a question to an llm it spits out a generic response format: ''' well, subject x has a lot of nuance filled with even more nuance. and it may be that x is true but y could also be true, here's a list of related sentences: 1. subject 1 is pretty broad in scope but applies to the question 2. subject 2 is more niche conceptually and applies to the core of the topic without addressing every aspect of it 3. and the list goes on ''' this is the technology you can't surpass?
- peeters 3y agoI'm guessing you haven't done much with prompt customization. Try starting a conversation with something like "I'm the research assistant for a policy maker and often need to summarize complex topics for my boss. Please avoid any fluff or boilerplate language in your responses, including not reiterating the question, and favor brevity while still using a paragraph structure instead of bullet points." You'd be surprised by how much different your answers will look.
- godelski 3y agoResearch papers definitely need to be more nuanced with the "zero-shot" language. Originally this term was used to describe out of distribution and out of class instances and in the context of metalearning (if you don't know, see under the history section I left for context). This term has been really bastardized and it makes it difficult to differentiate works now. "Out-of-domain" is a fuzzy concept and I think there are some weird usages where people would call something OOD but wouldn't call a test set OOD. OOD classically doesn't mean something not in training data, but not in the distribution of data your data is a proxy for. Certainly the data here is within distribution as it is using LLMs. > Our approach, Binoculars, is so named as we look at inputs through the lenses of two different language models. How is LLM generated data out of domain of LLMs? Specifically their github demonstrates with Falcon-7B and Falcon-7B-Instruct models. Instruct models are specifically tuned on their own outputs. We can even say the non-instruct models are also "trained on" LLM outputs as you're using the outputs in the calculation of the cost functions, meaning they see that data and are using that information, which is why > Unsurprisingly, LLMs tend to generate text that is unsurprising to an LLM. Because they are trained on cross-entropy which directly related to perplexity. Are detector researchers really trying to use perplexity to detect LM generation? That seems odd since that's dependent on the exact thing LMs are minimizing... It also seems weird because the premise from the paper is that human writing has more "surprise" than that from an LM, but we're instructing LMs to sound more human. Going about detection this way does not sound like it would be a sustainable method (not that LLM detectors are reliable and I think we all know they frequently flag generic or standard text, which of course they do if you're highly dependent on entropy). === History === First example I'm aware of is the "one-shot" case from[0] (2000) and abstract says > We suggest that this density over transforms may be shared by many classes, and demonstrate how using this density as “prior knowledge” can be used to develop a classifier based on only a single training example for each class. Which we can think of as taking a model and fine tuning (often now just called training) with a single epoch, relying on the prior knowledge that the model learned that is general to other tasks (such as training on cifar-10 should be a good starting point for classifying lions). Then come [1,2] in 2008. Where [1]'s title is "Importance of Semantic Representation: Dataless Classification" and [2] (from Yoshua Bengio's group) is "Zero-data Learning of New Tasks". [1] trains on Wikipedia and then tests semantic classification on a modified 20 Newsgroup dataset (expanded labels) and Yahoo Answers dataset and is about the generalizability of the embedding mechanism cross domain. Specifically they compared Bag of Words (BoW) to Explicit Semantic Analysis (ESA). I'll just quote for [2] > We tested the ability of the models to perform zero-data generalization by testing the discrimination ability between two character classes not found in the training set. Part of their experiments includes training on numeric character recognition and testing on alphabetical characters. They also do some low-shot experiments. [0] https://people.cs.umass.edu/~elm/papers/cvpr2000.pdf https://people.cs.umass.edu/~elm/papers/cvpr2000.pdf [1] https://citeseerx.ist.psu.edu/document?doi=ee0a332b4fc1e82a9999acd6cebceb165dc8645b https://citeseerx.ist.psu.edu/document?doi=ee0a332b4fc1e82a9... [2] https://cdn.aaai.org/AAAI/2008/AAAI08-103.pdf https://cdn.aaai.org/AAAI/2008/AAAI08-103.pdf
- akasakahakada 3y ago>>> despite not being trained on any ChatGPT data I doubt that since ChatGPT trained all other LLM.