Keyword density helper – This tool comes with a built-in keyword density helper in some ways similar to the likes of SurferSEO or MarketMuse the difference being, ours is free! This feature shows the user the frequency of single or two word keywords in a document, meaning you can easily compare an article you have written against a competitor to see the major differences in keyword densities. This is especially useful for SEO’s who are looking to optimize their blog content for search engines and improve the blog’s visibility.
File compare – Text comparison between files is a breeze with our tool. Simply select the files you would like to compare, hit “Upload” and our tool will automatically insert the content into the text area, then simply hit “Compare” and let our tool show you where the differences in the text are. By uploading a file, you can still check the keyword density in your content.
Comparing text between URLs is effortless with our tool. Simply paste the URL you would like to get the content from (in our example we use a fantastic blog post by Sherice Jacob found here) hit “Submit URL” and our tool will automatically retrieve the contents of the page and paste it into the text area, then simply click “Compare” and let our tool highlight the difference between the URLs. This feature is especially useful for checking keyword density between pages!
You can also easily compare text by copying and pasting it into each field, as demonstrated below.
Ease of use
Our text compare tool is created with the user in mind, it is designed to be accessible to everyone. Our tool allows users to upload files or enter a URL to extract text, this along with the lightweight design ensures a seamless experience. The interface is simple and straightforward, making it easy for users to compare text and detect the diff.
Multiple text file format support
Our tool provides support for a variety of different text files and microsoft word formats including pdf file, .docx, .odt, .doc, and .txt, giving users the ability to compare text from different sources with ease. This makes it a great solution for students, bloggers, and publishers who are looking for file comparison in different formats.
Protects intellectual property
Our text comparison tool helps you protect your intellectual property and helps prevent plagiarism. This tool provides an accurate comparison of texts, making it easy to ensure that your work is original and not copied from other sources. Our tool is a valuable resource for anyone looking to maintain the originality of their content.
User Data Privacy
Our text compare tool is secure and protects user data privacy. No data is ever saved to the tool, the users’ text is only scanned and pasted into the tool’s text area. This makes certain that users can use our tool with confidence, knowing their data is safe and secure.
Compatibility
Our text comparison tool is designed to work seamlessly across all size devices, ensuring maximum compatibility no matter your screen size. Whether you are using a large desktop monitor, a small laptop, a tablet or a smartphone, this tool adjusts to your screen size. This means that users can compare texts and detect the diff anywhere without the need for specialized hardware or software. This level of accessibility makes it an ideal solution for students or bloggers who value the originality of their work and need to compare text online anywhere at any time.
The world needs reliable AI detection tools, but no AI detection tool is ever going to be 100% perfect. Users should understand the individual limitations of these tools in regards to AI detector accuracy so that they can wield them responsibly, which means the developers of AI detectors should be as transparent as possible about the capabilities and limitations of their detectors.
Below is our own analysis of our detector’s efficacy. To review third-party data on Originality.ai's AI detector accuracy see this meta-analysis of multiple academic studies on AI text detection.
You can try the Originality.ai AI Detector for free here.
This guide aims to provide you with an answer to the question of: What AI content detector is the most accurate? Additionally, we are proposing a standard for testing AI detector effectiveness and AI detector accuracy, along with the release of an Open Source tool to help increase the transparency and accountability with all AI content detectors.
We hope to achieve this idealistic goal by…
If you have been asked or want to evaluate an AI content detector's potential use case for your organization, this article is for you.
This guide will help you understand AI detectors and their limitations by showing you…
If you have any questions, suggestions, research questions, or potential commercial use cases please contact us.
● You asked, and we listened. With the overwhelming appreciation for Lite, which allows for some light AI editing (think Grammarly), we’re making it our default model!
● Originality.ai Launches Version 3.0.1 Turbo (the most accurate AI detector ever) resulting in an improvement on the most challenging dataset created from the newest LLMs.
● Use Version 3.0.1 Turbo — If your risk tolerance for AI is ZERO! It is designed to identify any use of AI even light AI editing.
● Use Version Lite — If you want to minimize false positives and are okay with light AI editing.
● Across all tests Originality.ai has increased its accuracy, further establishing Originality.ai as the most accurate AI checker.
Do AI Detectors Work? OpenAI Says No???
Oversimplistic views that “AI detectors are perfect” or “AI detectors don't work” are equally bad.
We still have an offer to OpenAI (or anyone willing to take us up on it) to back up their claim that AI detectors don't work.
AI detection tools' “accuracy” should be communicated with the same transparency and accountability that we want to see in AI’s development and use. Our hope is this study will move us all closer to that ideal.
At Originality.ai we love AI-generated content… but believe in transparency and accountability in its development, use, and detection. Personally, I don’t want a writer or agency I have hired to create content for my audience and generate it with AI without my knowledge.
Originality.ai helps ensure there is trust in the originality of the content being produced by writers, students, job applicants or journalists.
Why is transparency and accountability important...
Claimed accuracy rates with no supporting studies are clearly a problem.
We hope the days of AI detection tools claiming 99%+ accuracy with no data to support it are over. A single number is not good enough in the face of the societal problems AI content can produce and the important role AI content detectors have to play.
The FTC has come out on multiple occasions to warn against tools claiming AI detection accuracy or unsubstantiated AI efficacy.
“If you’re selling a tool that purports to detect generative AI content, make sure that your claims accurately reflect the tool’s abilities and limitations. “ source
“you can’t assume perfection from automated detection tools. Please keep that principle in mind when making or seeing claims that a tool can reliably detect if content is AI-generated.” source
“Marketers should know that — for FTC enforcement purposes — false or unsubstantiated claims about a product’s efficacy are our bread and butter” source
We fully agree with the FTC on this and have provided the tool needed for others to replicate this study for themselves.
The misunderstanding of how to detect AI-generated content has already caused a significant amount of pain including a professor who incorrectly failed an entire class.
AI Content Detectors need to be a part of the solution to undetectable AI-generated content and the current unsupported AI detection accuracy claims and research papers that have tackled this problem are simply not good enough in the face of the societal risks LLM-generated content poses including…
Along with this study, we are releasing the latest version of our AI content detector. Below is our release history.
● 1.1 – Nov 2022 BETA (released before Chat-GPT)
● 1.4 – Apr 2023
● 2.0 Standard — Aug 2023
● 3.0 Turbo — Feb 2024
Even easier to use Open Source AI detection efficacy research tool released
● 2.0.1 Standard (BETA) — July 2024
● 1.0.0 Lite — July 2024
● 3.0.1 Turbo — October 2024
Our AI detector works by leveraging supervised learning of a carefully fine-tuned large AI language model.
We use a large language model (LLM) and then feed this model millions of carefully selected records of known AI and known human content. It has learned to recognize patterns between the two.
More details on our AI content detection.
Below is a brief summary of the 3 general approaches that an AI detector (or called in Machine Learning speak a “classifier”) can use to distinguish between AI-generated and human-generated text.
1. Feature-Based Approach:
2. Zero-Shot Approach:
3. Fine Tuning AI Model Approach:
The test below looks at the performance of multiple detectors using all of the strategies identified above.
This post covers four main tests and some supporting tests that were all completed on the latest version of the Originality.ai AI Content Detector.
One test involved hundreds of thousands of samples and looked at Originality V1.4 vs V2.0 vs V3.0, while the second proposes a smaller Challenging Benchmark Dataset, and we compared multiple AI content detectors' performance against that dataset. The third uses a published open-source dataset for testing AI content detectors effectiveness.
Tests on the second and third datasets were run the week of July 24, all using our open-sourced AI content detection accuracy tool or if an API was not available via humans entering the text and recording the results.
The second test can be replicated using the benchmark dataset and our open-sourced tool.
The 4th test is a series of tests completed on other publicly available datasets testing Originality.ai’s effectiveness.
In the spirit of openness and contributing to the understanding of the effectiveness and limitations of AI detectors we are open-sourcing this “challenging” benchmark dataset to help with the evaluation of different AI detection methods. If someone was working to make AI writing undetectable, this is the type of content that they would produce.
This benchmark dataset includes samples from some of the most challenging prompts and settings for LLM models including ChatGPT4, GPT-4, and Paraphrasers etc. Additionally, it includes known human content.
The table below shows the datasets and a brief explanation of each.
Download the dataset here
The dataset(s) provided might be applicable for your use case or potentially if you are evaluating AI detection tools effectiveness for another type of content you will need to produce your own dataset. For example, I would not rely solely on these results if you are looking for an AI detector to identify fake social media messages or online reviews. Use our Open-Source Tool to make running your data and evaluating detectors' performance much easier.
To make the running of tests easy, repeatable and accurate we created and decided to open-source our tools to help others do the same. The main tool allows you to enter the API key for multiple AI content detectors and plug in your own data to then receive not just the results from the tool but also a complete statistical analysis of the detection effectiveness calculations.
This tool makes it incredibly easy for you to run your test content against all AI content detectors that have an available API.
The reason we built and open-sourced this tool to run tests is so that we can increase the transparency into tests by…
The speed at which new LLMs are launching and the speed AI detection is evolving means that accuracy studies which take 4 months from test to publication are hopelessly outdated.
Features of This Tool:
Link to GitHub: https://github.com/OriginalityAI/AI-detector-research-tool
In addition to the tool mentioned above, we have provided three additional ways to easily run a dataset through our tool…
We do not believe that AI detection scores alone should be used for academic honesty purposes and disciplinary action.
The rate of false positives (even if low) is still too high to be relied upon for disciplinary action.
Here is a guide we created to help writers or students reduce false positives in AI content detector usage. Plus, we created a free AI detector Chrome extension to help writers/editors/students/teachers visualize the creation process and prove originality.
Our newly released Version 1.0.0 Lite model is best for educators and academic settings as it allows for light AI editing with popular tools like Grammarly (grammar and spelling suggestions).
Below are the best practices and methods used to evaluate the effectiveness of AI classifiers (i.e., AI content detectors). There is some nerdy data below, but if you are looking for even more info, here is a good primer for evaluating the performance of a classifier.
One single number related to a detector's effectiveness without additional context is useless!
Don’t trust a SINGLE “accuracy” number without additional context.
Here are the metrics we look at to evaluate a detector's efficacy…
The confusion matrix and the F1 (more on it later) together are the most important measures we look at. In one image you can quickly see the ability of an AI model to correctly identify both Original and AI-generated content.
Version 1.4 Confusion Matrix on a GPT-4 & Human Dataset Test
Identifies AI content correctly x% of the time. True Positive Rate TPR (also known as sensitivity, hit rate or recall).
Identifies human content correctly x% of the time. True Negative Rate TNR (also known as specificity or selectivity).
What % of your predictions were correct? Accuracy alone can provide a misleading number. This is in part why you should be skeptical of AI detectors' claimed “accuracy” numbers if they do not provide additional details for their accuracy numbers. The following metric is what we use, along with our open source tool to measure accuracy..
Combines Recall and Precision to create one measure to rank all detectors, often used when ranking multiple models. It calculates the harmonic mean of precision and sensitivity.
So, what should and should not be considered AI content? As “cyborg” writing combining humans and AI assistants rise what should and shouldn’t be considered AI content is tricky!
Some studies have made some really strange decisions on what to claim as “ground truth” human or AI-generated content.
In fact, there was one study that used human-written text in multiple languages that were then translated (using AI tools) to English and called it “ground truth” Human content.
Source…
Description of Dataset:
Classifying the AI Translated Dataset (02-MT) as Human-written???
https://arxiv.org/pdf/2306.15666.pdf
We think this approach is crazy!
Our position is that if the effect of putting content into a machine is that the output from that machine is unrecognizable when comparing the two documents then it should be the aim of an AI detector to identify the output text as AI generated.
The alternative is that any content could be translated and presented as Original work since it would pass both AI and plagiarism detection.
Here is what we think should and should not be classified as AI-generated content:
*AI Outline is defined as using AI (an LLM) to create a content idea, do some research, and/or create an outline. The level at which AI is used during this process may vary and could potentially affect the likelihood the text is detected as AI or human
Some journalists, such as Kristi Hines, have done a great job at trying to evaluate what AI content is and whether AI content detectors should be trusted by reviewing several studies - https://www.searchenginejournal.com/should-you-trust-an-ai-detector/491949/.
Review a meta-analysis of AI-detector accuracy studies for further insight into the efficacy of AI-detectors.
Finally! let's get to the tests.
As new and improved LLMs are released, we need to update our models and our benchmark testing.
Our July 2024 release also includes the launch of Version Lite 1.0.0; see the results from our benchmark testing of the new Lite model.
1. General Performance: The Lite model focuses on accurately identifying human-written content (or minimizing the False Positive rate) and exhibits outstanding performance on ‘human’ and ‘AI-lightly-edited’ sources.
2. Dataset-Specific Performance:
3. Key Findings — Exceptional False Positive Rate and Excels at Identifying Lightly AI-Edited Content
The accuracy of Version Lite 1.0.0 in detecting content which is lightly edited with AI tools such as Grammarly’s suggestions with a low false positive rate, makes it an asset for academic and educational settings.
In the next tests we will look at the performance of many AI content detectors to evaluate their relative effectiveness.
To complete the tests and make them repeatable for others to execute, we used…
For Test #2 it is important to remember this is a “Challenging” dataset with adversarial settings on GPT-3, GPT-4, ChatGPT and the Paraphraser data. It is not an accurate reflection of AI detection tools on most “generic” AI-generated content.
Results, Including Data and Scores, Can be downloaded and viewed here:
Note: This test was completed with 2.0.0 Standard; this model is being retired.
See the updated results for Lite, our newest model, below.
Human Data Entry vs API: We did not have API access to several tools and had a team manually checking the results, this could introduce errors. For the tools ContentAtScale, TurnItIn, and WinstonAI the results could have some human error. False positives were double-checked.
Dataset Quality: This benchmark dataset came from a MUCH larger dataset and did not get a human review to clean it. The result is that there are some entries that are not great samples.
New Updates to Detectors: Our model was run on Feb 13th 2024 and all other tests were run within a 1 week window between July 24th-July 28th 2023 but these results are a snapshot of a moment-in-time performance and not reflective of future performance.
Limited Dataset Size: As our AI research team wrote, 2000 samples should not be considered a conclusive efficacy test.
If you would like to run your own or other datasets to test the accuracy of AI detectors easily, you can use our Open Source tool and pick any of the datasets below…
Here are some additional datasets that you can use in your own testing.
We did not run ALL the tools through these datasets but did run Originality.ai through each of them, and have shared the results for how Originality performed below.
Each of these datasets comes from a publicly available research paper.
Below, Models Lite and Turbo are compared.
Studies/Dataset we chose not to list face similar issues…
Here are 6 additional studies completed by 3rd parties and their findings showing Originality to be the most accurate…
The end result is we have run tests across our own dataset and all publicly available datasets which continue to demonstrate the efficacy of Originality.ai AI detection.
Below is a list of all AI content detectors and a link to a review of each. For a more thorough comparison of all AI detectors and their features, have a look at this post: 22 AI Content Detection Tools
List of Tools:
As these tests have shown not all tools are created equal! There have been many quickly created tools that simply use a popular Open Source GPT-2 detector (195k downloads last month).
Below are a few of the main reasons we suspect Originality.ai’s AI detection performance and overall AI detector accuracy are significantly better than alternatives…
The AI/ML team and core product team at Originality.ai have worked relentlessly to build and improve on the most effective AI content detector!
The Results…
● Originality.ai Launches Lite 1.0.0.
● Originality.ai Launches Version 3.0.1 Turbo
● Across 6 Datasets, Originality’s Latest Version Was the Most Accurate & Effective Detector in Each Test
● 6 AI Content Detectors Were Tested on a new, Challenging Benchmark Dataset, with Originality.ai being the most accurate
● Open Source Tool and Benchmark Dataset for Efficient Detector Testing Developed and Released
We hope this post will help you understand more about AI detectors, AI detector accuracy, and give you the tools to complete your own analysis if you want to.
We believe…
Our hope is this study has moved us closer to achieving this and that our open-source initiatives will help others to be able to do the same.
If you have any questions on whether Originality.ai would be the right solution for your organization, please contact us.
If you are looking to run your own tests, please contact us. We are always happy to support any study (academic, journalist, or curious mind).
Additionally, to learn more about how Originality.ai performs in third-party academic research and studies, review our meta-analysis of accuracy studies.
Try our AI detector for yourself.
No, that’s one of the benefits, only fill out the areas which you think will be relevant to the prompts you require.
When making the tool we had to make each prompt as general as possible to be able to include every kind of input. Not to worry though ChatGPT is smart and will still understand the prompt.
Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.
Vahan Petrosyan
searchenginejournal.com
I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.
Tom Demers
searchengineland.com
After extensive research and testing, we determined Originality.ai to be the most accurate technology.
Rock Content Team
rockcontent.com
Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.
Felix Rose-Collins
ranktracker.com
ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.ai
Ashley Stahl
forbes.com
Originality.ai Do give them a shot!
Sri Krishna
venturebeat.com
For web publishers, Originality.ai will enable you to scan your content seamlessly, see who has checked it previously, and detect if an AI-powered tool was implored.
Industry Trends
analyticsinsight.net
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
Text comparison tools work by analyzing and comparing the contents of two or more text documents to find similarities and differences between them. This is typically done by breaking the texts down into smaller units such as sentences or phrases, and then calculating a similarity score based on the number of identical or nearly identical units. The comparison may be based on the exact wording of the text, or it may take into account synonyms and other variations in language. The results of the comparison are usually presented in the form of a report or visual representation, highlighting the similarities and differences between the texts.
String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
The most basic form of string comparison is the equality test, where the two strings are compared character by character and a Boolean result indicating whether they are equal or not is returned. More sophisticated string comparison algorithms use heuristics and statistical models to determine the similarity between two strings, even if they are not exactly the same. These algorithms often use techniques such as edit distance, which measures the minimum number of operations (such as insertions, deletions, and substitutions) required to transform one string into another.
Another common technique for string comparison is n-gram analysis, where the strings are divided into overlapping sequences of characters (n-grams) and the frequency of each n-gram is compared between the two strings. This allows for a more nuanced comparison that takes into account partial similarities, rather than just exact matches.
String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
With our tool it’s easy, just enter or upload some text, click on the button “Compare text” and the tool will automatically display the diff between the two texts.
Using text comparison tools is much easier, more efficient, and more reliable than proofreading a piece of text by hand. Eliminate the risk of human error by using a tool to detect and display the text difference within seconds.
We have support for the file extensions .pdf, .docx, .odt, .doc and .txt. You can also enter your text or copy and paste text to compare.
There is never any data saved by the tool, when you hit “Upload” we are just scanning the text and pasting it into our text area so with our text compare tool, no data ever enters our servers.
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This table below shows a heat map of features on other sites compared to ours as you can see we almost have greens across the board!
