PriorArtAI: 'ChatGPT on ChatGPT Bakes'
Summary
Prior art refers to existing knowledge in patent and copyright law. Temperature is a parameter controlling the creativity and coherence of text generated by models like ChatGPT which is a Large Language Model (LLM).
Analysing and comparing original texts generated at different temperatures can provide insights into the effects of temperature on the original text. One could then compare and contrast original text at different temperatures with a suspected plagiarist's text.
The substantial similarity of the original text 'baked' at a particular temperature could lead one to investigate wether the source of the suspected text had access to the original text, and wether a case for action was necessary.
Analysing and comparing original texts generated at different temperatures can provide insights into the effects of temperature on the original text. One could then compare and contrast original text at different temperatures with a suspected plagiarist's text.
The substantial similarity of the original text 'baked' at a particular temperature could lead one to investigate wether the source of the suspected text had access to the original text, and wether a case for action was necessary.
Prior Art
"Prior art" is a term used in patent law that refers to all information that has been made available to the public in any form before a given date that might be relevant to a patent's claims of originality. If an invention has been described in prior art, a patent on that invention is not valid.
The purpose of prior art is to prevent the patenting of inventions that are not truly novel. This can include any public document or public activity, including patents, published patent applications, academic papers, public presentations, trade catalogs, public uses, sales, or anything else that demonstrates the invention was already known.
In the context of copyright law, "prior art" doesn't carry the same meaning as in patent law. Copyright law protects original works of authorship, and there is no requirement for a work to be novel or non-obvious like in patent law. Instead, the work must be independently created and possess a minimal degree of creativity.
In a broader sense, "prior art" can also refer to the body of knowledge that existed prior to a new creation, which could be relevant in determining originality or influence.
The purpose of prior art is to prevent the patenting of inventions that are not truly novel. This can include any public document or public activity, including patents, published patent applications, academic papers, public presentations, trade catalogs, public uses, sales, or anything else that demonstrates the invention was already known.
In the context of copyright law, "prior art" doesn't carry the same meaning as in patent law. Copyright law protects original works of authorship, and there is no requirement for a work to be novel or non-obvious like in patent law. Instead, the work must be independently created and possess a minimal degree of creativity.
In a broader sense, "prior art" can also refer to the body of knowledge that existed prior to a new creation, which could be relevant in determining originality or influence.
Introduction to Temperature
In the context of text generation models like GPT-3 and GPT-4, "temperature" is a hyperparameter that controls the randomness of the model's outputs.
When the temperature is set to a high value (e.g., close to 1), the model generates more random outputs. It's more likely to choose less probable words, leading to more diverse and creative text, but at the risk of producing outputs that don't make as much sense.
When the temperature is set to a low value (e.g., close to 0), the model generates less random outputs. It's more likely to choose the most probable word at each step, leading to more predictable and coherent text, but at the risk of being less creative or diverse.
So, in a sense, the temperature parameter allows you to control the trade-off between creativity/diversity and coherence/predictability in the model's outputs. This can be particularly useful when fine-tuning the model for specific tasks or applications.
When the temperature is set to a high value (e.g., close to 1), the model generates more random outputs. It's more likely to choose less probable words, leading to more diverse and creative text, but at the risk of producing outputs that don't make as much sense.
When the temperature is set to a low value (e.g., close to 0), the model generates less random outputs. It's more likely to choose the most probable word at each step, leading to more predictable and coherent text, but at the risk of being less creative or diverse.
So, in a sense, the temperature parameter allows you to control the trade-off between creativity/diversity and coherence/predictability in the model's outputs. This can be particularly useful when fine-tuning the model for specific tasks or applications.
A Deeper Dive into Temperature: Creativity By Another Name
Other phrases used to describe temperature might include 'diversity', 'exploration factor', or 'randomness parameter'.
The temperature value is used when the model is choosing from a probability distribution of possible next words. If the temperature is set to a high value (e.g., 1.0 or higher), the model's output will be more random: it will select from a wider range of potential next words. If the temperature is set to a low value (e.g., 0.1), the model's output will be more deterministic: it will tend to select the most likely next word.
To change the temperature and therefore the randomness or determinism of the model's output, you simply adjust the temperature parameter when generating text. This can be done in the function or method that's used for text generation.
In terms of how ChatGPT defines the temperature of text, it doesn't really 'define' the temperature. Rather, it uses the temperature parameter that's set when text is being generated. In essence the person asking for the text to be generated uses temperature to determine 'creativeness' There isn't a before and after in this context – the temperature is just a parameter that's used during text generation, not something that's applied to the text afterwards. Much like creativity.
Mathematically, the temperature is used in a process called temperature scaling, which adjusts the probability distribution of the next word. Given a distribution of logits (outputs from the model before applying the softmax function to get probabilities), each logit is divided by the temperature value. Then, softmax is applied to these adjusted logits to get the final probabilities. A higher temperature makes the logits closer together, leading to a more uniform (random) distribution, while a lower temperature makes the logits more extreme, leading to a more peaky (deterministic) distribution.
To document the temperature of a text, you would need to keep track of the temperature value that was used when generating the text. Since the temperature is a parameter of the text generation process, it's not something that can be determined from the text alone after the fact. It would be useful to note down the temperature used along with other key parameters like the seed text or prompt, the model architecture and version, and any other relevant settings or conditions.
The temperature value is used when the model is choosing from a probability distribution of possible next words. If the temperature is set to a high value (e.g., 1.0 or higher), the model's output will be more random: it will select from a wider range of potential next words. If the temperature is set to a low value (e.g., 0.1), the model's output will be more deterministic: it will tend to select the most likely next word.
To change the temperature and therefore the randomness or determinism of the model's output, you simply adjust the temperature parameter when generating text. This can be done in the function or method that's used for text generation.
In terms of how ChatGPT defines the temperature of text, it doesn't really 'define' the temperature. Rather, it uses the temperature parameter that's set when text is being generated. In essence the person asking for the text to be generated uses temperature to determine 'creativeness' There isn't a before and after in this context – the temperature is just a parameter that's used during text generation, not something that's applied to the text afterwards. Much like creativity.
Mathematically, the temperature is used in a process called temperature scaling, which adjusts the probability distribution of the next word. Given a distribution of logits (outputs from the model before applying the softmax function to get probabilities), each logit is divided by the temperature value. Then, softmax is applied to these adjusted logits to get the final probabilities. A higher temperature makes the logits closer together, leading to a more uniform (random) distribution, while a lower temperature makes the logits more extreme, leading to a more peaky (deterministic) distribution.
To document the temperature of a text, you would need to keep track of the temperature value that was used when generating the text. Since the temperature is a parameter of the text generation process, it's not something that can be determined from the text alone after the fact. It would be useful to note down the temperature used along with other key parameters like the seed text or prompt, the model architecture and version, and any other relevant settings or conditions.
The Proof of the Bake is in the Eating
It's important to clarify that "temperature" isn't a property of a text per se, it's a parameter used in the process of generating text. So, you can't directly measure the "temperature" of a text that's already been generated, in the same way you can't measure the "temperature" of a cake that's already been baked. The temperature parameter influences the process, not the end product.
That being said, you might infer that a high temperature was used if the text seems more random or unpredictable, and that a low temperature was used if the text seems more deterministic or focused. But this would be a rough inference at best, and it could easily be misled by other factors, like the prompt that was used, the specific model architecture and training data, etc.
If you want to compare the effects of different temperature settings, a better approach would be to generate multiple versions of a text using different temperature values, and then compare the results. The inputs would be the seed text or prompt, the temperature values, and any other generation parameters, and the outputs would be the generated texts.
You could then analyze these texts in terms of their diversity, coherence, novelty, etc., which might give you some insight into how the temperature parameter is influencing the generation process. This would require a careful and systematic analysis, and probably some knowledge of natural language processing and statistics.
That being said, you might infer that a high temperature was used if the text seems more random or unpredictable, and that a low temperature was used if the text seems more deterministic or focused. But this would be a rough inference at best, and it could easily be misled by other factors, like the prompt that was used, the specific model architecture and training data, etc.
If you want to compare the effects of different temperature settings, a better approach would be to generate multiple versions of a text using different temperature values, and then compare the results. The inputs would be the seed text or prompt, the temperature values, and any other generation parameters, and the outputs would be the generated texts.
You could then analyze these texts in terms of their diversity, coherence, novelty, etc., which might give you some insight into how the temperature parameter is influencing the generation process. This would require a careful and systematic analysis, and probably some knowledge of natural language processing and statistics.
Who is Eating Your Cake
Let's assume an author's representatives has, with the necessary permissions, input the authors original text into ChatGPT (or other LLM) asking it to recreate the text at different temperatures. Then assume the outputs of this are process is text at range of incremental temperatures: temperature(0) to Temperature(n). Where (0) is the lowest temperature and (n) is largest temperature available in the LLM.
One then could estimate the likelihood that a 'suspect' text was generated by a model like ChatGPT (or other LLM) by comparing it to the original text at different temperatures.
The process would involve comparing the stylistic and content-based features of the original text (at different temperatures)
Here's a general outline of such a process:
One then could estimate the likelihood that a 'suspect' text was generated by a model like ChatGPT (or other LLM) by comparing it to the original text at different temperatures.
The process would involve comparing the stylistic and content-based features of the original text (at different temperatures)
Here's a general outline of such a process:
- Preprocessing: Clean and preprocess both the author's original text and the suspected generated text. This could involve removing punctuation, converting to lowercase, tokenising, etc.
- Feature Extraction: Extract features from both texts. This could include syntactic features like sentence length and complexity, lexical features like word usage patterns, and semantic features like topic distribution. You could also compare specific phrases or sentences between the two texts to identify any directly copied sections
- Stylometric Analysis: Perform a stylometric analysis to compare the stylistic features of the two texts. Stylometry is the quantitative study of literary style through various statistical methods. This could involve analysing the distribution of word and phrase lengths, the usage of different parts of speech, the complexity of sentence structures, etc.
- Text Generation Simulation: Generate new text from the original author's text using ChatGPT with various temperature settings. Compare the generated text with the suspected generated text to identify any patterns or similarities.
- Machine Learning Classification: Train a machine learning model to distinguish between the author's original text and text generated by ChatGPT. This could involve using the features extracted in steps 2 and 3 as input features for the model. Test the model on the suspected generated text to see if it's classified as 'original' or 'generated'.
- Reporting: Create a report summarising the results of the analysis. This could include visualisations of the feature comparisons, the results of the stylometric analysis and the machine learning classification, and any other relevant information.
