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Introduction:
Are you tired of sifting through countless online reviews, comments, and news articles, wondering if they were genuinely written by humans?
Introducing GLTR, the groundbreaking tool developed by the MIT-IBM Watson AI lab and HarvardNLP. With its forensic analysis capabilities, GLTR can detect auto-generated text, providing a vital solution in the era of advanced language models.
By visually analyzing the output of the GPT-2 117M language model, GLTR ranks each word based on its likelihood of being artificially generated. It highlights the most probable words in green, followed by yellow and red, making it effortless to identify computer-generated text.
But GLTR doesn’t stop there. It goes beyond visual cues by presenting three informative histograms that aggregate information over the entire text. These histograms offer additional evidence, revealing the distribution of word categories, the ratio between predicted words, and the distribution of prediction entropies.
With GLTR, you can now uncover fake reviews, comments, and news articles generated by large language models. Its live demo and open-source code on Github provide accessibility to researchers and curious minds alike. Don’t miss out on this powerful tool that was even nominated for the best demo at ACL 2019.
Overview:
GLTR is an AI tool developed by the MIT-IBM Watson AI lab and HarvardNLP that utilizes forensic analysis to detect automatically generated text. It specifically focuses on identifying texts that have been artificially generated by analyzing the likelihood of a language model producing the text.
By visually analyzing the output of the GPT-2 117M language model from OpenAI, GLTR is able to rank each word based on its probability of being generated by the model. This ranking is represented through color highlighting, with the most likely words shown in green, followed by yellow and red, while the remaining words are displayed in purple.
This visual indication allows for easy identification of computer-generated text. Additionally, GLTR provides three histograms that aggregate information over the entire text. The first histogram displays the frequency of words in each category, the second illustrates the ratio between the probabilities of the top predicted word and the subsequent word, and the third shows the distribution of predictions’ entropies. These histograms provide further evidence to determine whether a text has been artificially generated.
GLTR is particularly useful in detecting fake reviews, comments, or news articles that are generated by large language models, which can produce text that is nearly indistinguishable from human-written content to non-expert readers. The tool is accessible through a live demo and its source code is available on Github. Researchers can also refer to the ACL 2019 demo track paper, which was nominated for best demo.
Benefits:
- GLTR is a tool developed by the MIT-IBM Watson AI lab and HarvardNLP.
- It can detect automatically generated text using forensic analysis.
- GLTR visually analyzes the output of the GPT-2 117M language model from OpenAI.
- The tool highlights the most likely words in green, followed by yellow and red, and the rest of the words in purple.
- GLTR provides a direct visual indication of how likely each word was under the model, making it easy to identify computer-generated text.
- GLTR also shows three histograms which aggregate information over the whole text.
- The first histogram shows how many words of each category appear in the text.
- The second illustrates the ratio between the probabilities of the top predicted word and the following word.
- The third shows the distribution over the entropies of the predictions.
- By analyzing these histograms, GLTR provides additional evidence of whether a text has been artificially generated.
- GLTR can be used to detect fake reviews, comments, or news articles generated by large language models.
- It can be accessed through a live demo and the source code is available on Github.
- Researchers can also read the ACL 2019 demo track paper, which was nominated for best demo.
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