Detecting AI-Generated Scientific Texts Using Sentiment Analysis and Random Forest Classification

Centrala begrepp

A machine learning approach using sentiment analysis features and a random forest classifier can effectively detect whether a scientific text was generated by a large language model (LLM) like ChatGPT or written by a human.

Sammanfattning

The paper proposes a new methodology to classify scientific texts as either human-written or generated by a large language model (LLM) like ChatGPT. The key aspects of the approach are:

Data Ingestion and Preparation:

Collected 68 recent papers from the "New Phytologist" journal and their abstracts.
Generated equivalent abstracts for each paper using ChatGPT v3.
Preprocessed the texts by removing stop words and applying stemming.

Feature Engineering:

Performed sentiment analysis on the texts using four different lexicons (Bing, Afinn, NRC, Loughran-McDonald).
Derived various sentiment-based features, such as ratios of positive/negative words, average/standard deviation of sentiment scores, etc.

Model Training:

Used a random forest classifier to train a model to distinguish human-written and AI-generated texts based on the sentiment analysis features.
Employed a stratified 10-fold cross-validation approach due to the limited dataset size.

Results:

The trained random forest model achieved an accuracy of 84.14% in correctly classifying the texts.
The model showed a balanced performance across the two classes, with similar F-measure, Matthews Correlation Coefficient, and ROC/PRC area values.
The results suggest that sentiment analysis-based features can be a promising approach for building robust detectors to identify AI-generated scientific texts.

Statistik

The number of correctly classified instances is 122 out of 145 total instances.
The relative absolute error is 60.31%.
The root relative squared error is 74.46%.

Citat

"After the launch of ChatGPT v.4 there has been a global vivid discussion on the ability of this artificial intelligence powered platform and some other similar ones for the automatic production of all kinds of texts, including scientific and technical texts."
"For every computer scientist it is very well known the Turing Test, or the Imitation Game, as Alan Turing called it, as a milestone where we could acknowledge that Artificial Intelligence is really here."
"Academic institutions are particularly aware of the importance of this technological leap. For example, producing a scientific paper or a technical report on the topic one may think of, is a matter of just seconds using this platforms."

Viktiga insikter från

Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts

by Javier J. Sa... på arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08673.pdf

Djupare frågor

How can the proposed methodology be extended to handle more advanced LLMs like GPT-4 and future iterations?

To extend the proposed methodology to handle more advanced LLMs like GPT-4 and future iterations, several steps can be taken:

Data Collection and Preparation: Gather a larger and more diverse dataset that includes texts generated by GPT-4 and other advanced LLMs. Ensure that the dataset covers a wide range of topics and writing styles to capture the nuances of different LLM outputs.

Feature Engineering: In addition to sentiment analysis, incorporate other linguistic features such as syntactic complexity, vocabulary richness, coherence measures, and stylistic elements. These features can provide deeper insights into the differences between human-written and LLM-generated texts.

Model Training and Evaluation: Train the random forest classifier on the expanded dataset with the new features. Use techniques like cross-validation and hyperparameter tuning to optimize the model's performance. Evaluate the model's accuracy, precision, recall, and F1 score to ensure robustness.

Adaptation to New LLM Architectures: Stay updated on the advancements in LLM architectures and adjust the feature engineering and model training process accordingly. This may involve fine-tuning the feature selection process and retraining the classifier to accommodate the unique characteristics of each LLM version.

Integration of Explainable AI Techniques: Incorporate explainable AI techniques to understand the model's decision-making process when distinguishing between human and LLM-generated texts. This transparency can enhance the model's interpretability and trustworthiness.

What other feature engineering approaches, beyond sentiment analysis, could be combined with the random forest classifier to further improve the detection accuracy?

Beyond sentiment analysis, several feature engineering approaches can be combined with the random forest classifier to enhance the detection accuracy of human-written versus AI-generated texts:

Semantic Features: Include semantic similarity measures between words, phrases, or sentences in the text. Utilize techniques like Word2Vec or GloVe embeddings to capture semantic relationships and context within the text.

Structural Features: Incorporate structural characteristics such as sentence length, paragraph organization, use of headings, and overall document structure. These features can help differentiate between human-crafted and LLM-generated texts based on their organization and coherence.

Stylistic Features: Integrate stylistic elements like tone, writing style, use of specific vocabulary, and rhetorical devices. Analyzing these features can reveal subtle differences in writing patterns that distinguish human authors from LLMs.

Complexity Metrics: Include metrics related to text complexity, such as readability scores, sentence complexity, and vocabulary diversity. Human-written texts often exhibit more varied and nuanced language usage compared to LLM-generated content.

Contextual Features: Consider contextual information such as domain-specific terminology, references to current events, or cultural nuances. These features can help identify text elements that are more likely to be produced by human authors.

By combining these diverse feature engineering approaches with the random forest classifier, the model can leverage a broader set of indicators to accurately classify texts based on their origin.

How might the insights from this study on distinguishing human-written and AI-generated scientific texts be applied to other domains, such as creative writing or journalism, where the impact of LLMs is also significant?

The insights gained from distinguishing human-written and AI-generated scientific texts can be extrapolated to other domains like creative writing and journalism in the following ways:

Plagiarism Detection: The methodology developed for detecting AI-generated content can be adapted for plagiarism detection in creative writing and journalism. By identifying patterns and linguistic cues specific to LLM-generated texts, plagiarism detection tools can efficiently flag instances of content replication.

Content Verification: In journalism, where accuracy and authenticity are paramount, the model can be used to verify the source of news articles or reports. By differentiating between human-written and AI-generated content, journalists can ensure the credibility and integrity of the information they publish.

Quality Assessment: In creative writing, the model can assist in assessing the quality and originality of literary works. By analyzing the linguistic features and stylistic elements, authors and publishers can evaluate the authenticity and creativity of written pieces.

Editorial Assistance: Writers and editors can leverage the model to identify sections of text that may have been generated by LLMs, allowing them to review and refine those areas for coherence and consistency with the overall narrative.

Ethical Considerations: The study's findings can also inform discussions on the ethical implications of using AI in content creation. By understanding how to distinguish between human and AI-generated texts, stakeholders in creative writing and journalism can navigate the ethical challenges associated with automated content generation.

By applying the insights from this study across diverse domains, stakeholders can harness the potential of LLMs while upholding standards of authenticity, creativity, and ethical content creation.