AI-Generated Text Boundary Detection: Identifying the Transition from Human-Written to Machine-Generated Content

Centrala begrepp

This work addresses the task of detecting the boundary between human-written and machine-generated parts in texts that combine both. The authors evaluate several approaches, including perplexity-based methods, topological data analysis, and fine-tuned language models, on the RoFT and RoFT-chatgpt datasets. They find that perplexity-based classifiers outperform fine-tuned language models in cross-domain and cross-model settings, and analyze the properties of the data that influence the performance of different detection methods.

Sammanfattning

The authors investigate the task of detecting the boundary between human-written and machine-generated parts in texts that contain a mix of both. They experiment with several techniques, including:

Perplexity-based methods: The authors calculate sentence-level perplexity using various language models (e.g., GPT-2, Phi-1, Phi-1.5, Phi-2, LLaMA-2-7B) and use the perplexity features to train classifiers and regressors. They find that perplexity-based approaches tend to be more robust to domain-specific data than fine-tuned language models.

Topological data analysis (TDA): The authors explore the use of intrinsic dimension (ID) features extracted from RoBERTa token embeddings, treating them as time series data. They introduce two TDA-based models: "PHD + TS ML" and "TLE + TS Binary".

Fine-tuned RoBERTa: The authors fine-tune the RoBERTa model on the task and find that it outperforms other methods on the original RoFT dataset, but struggles with cross-domain generalization.

The authors also introduce a new dataset, RoFT-chatgpt, which extends the original RoFT with text generated by the GPT-3.5-turbo model. They analyze the performance of the different approaches on both datasets and find that perplexity-based classifiers generally outperform other methods in cross-domain and cross-model settings.
The authors further investigate the properties of the data that influence the performance of the detection methods, including sentence length distributions, label distributions, text structure, semantic and grammar inconsistencies, and discourse structure. They find that these data properties can significantly impact the performance of the classifiers, leading to both successes and failures in different settings.

Statistik

The average sentence length in human-written text is significantly different from machine-generated text.
The label distribution (i.e., the number of sentences before the boundary) varies across different language models used to generate the text.
Recipes and other structured text formats pose challenges for the classifiers, as the text structure can be viewed as an "adversarial" example for AI-generated text.
Semantic and grammar inconsistencies in text generated by simpler models (e.g., GPT-2, baseline) can make them easier for human raters to detect, but harder for supervised classifiers.
The underlying discourse structure of human-generated narratives, with peaks in perplexity corresponding to plot twists or changes in narrative focus, can confuse perplexity-based boundary detection.

Citat

"Perplexity-based classifiers are the best in terms of accuracy, while the perplexity regressor provides good MSE values."
"We have found that although RoBERTa-based classifiers demonstrate excellent results for in-domain classification, they lose to perplexity-based methods when tested on texts with new styles and topics not present in the training set."
"Including synthetic data in the pre-training dataset improves the performance of the detector."

Viktiga insikter från

AI-generated text boundary detection with RoFT

by Laida Kushna... på arxiv.org 04-04-2024

https://arxiv.org/pdf/2311.08349.pdf

Djupare frågor

How can the insights from this work be applied to develop more robust and generalizable artificial text detection systems that can handle a wider range of real-world scenarios?

The insights from this study can be instrumental in enhancing the robustness and generalizability of artificial text detection systems. One key takeaway is the effectiveness of perplexity-based classifiers in detecting boundaries between human-written and machine-generated text. By pre-training these classifiers on a diverse set of synthetic data from different generators, they can better adapt to various text styles and topics, improving their performance in real-world scenarios. Additionally, leveraging smaller language models trained on data generated by larger models can serve as strong features for boundary detection models, leading to higher accuracy and cross-domain robustness.
To further enhance the performance of artificial text detection systems, researchers can explore the use of topological features based on intrinsic dimensionality. These features have shown promising results in terms of robustness to domain shifts and model shifts. By incorporating topological data analysis techniques, such as persistent homology, into detection models, it is possible to capture geometric variations in token sequences that can aid in identifying AI-generated text more effectively.
Moreover, considering the impact of data properties such as sentence length distributions, label variations across models, text structure, semantic and grammar inconsistencies, and discourse structure can help in designing more comprehensive detection systems. By addressing these challenges and incorporating relevant features into the models, artificial text detection systems can be better equipped to handle a wider range of real-world scenarios with varying text styles, topics, and quality levels.

How can the insights from this work be applied to develop more robust and generalizable artificial text detection systems that can handle a wider range of real-world scenarios?

To improve the performance of boundary detection models, especially in cross-domain and cross-model settings, researchers can explore additional data properties and features beyond those explored in this study. Some potential avenues for enhancing the models include:

Syntactic and Semantic Analysis: Incorporating syntactic and semantic analysis techniques can help in identifying inconsistencies in language use and structure between human-written and machine-generated text. Features related to grammar, syntax, and semantic coherence can provide valuable insights for boundary detection models.

Contextual Embeddings: Utilizing contextual embeddings from pre-trained language models like BERT, RoBERTa, or GPT can enhance the understanding of text context and improve the detection of boundaries between human and machine-generated text. These embeddings capture rich contextual information that can aid in distinguishing between different text origins.

Stylistic Features: Considering stylistic features such as tone, writing style, and vocabulary choice can further enhance the performance of boundary detection models. By analyzing the stylistic differences between human and machine-generated text, the models can better identify the transition points in the text.

Multimodal Data Fusion: Integrating information from multiple modalities, such as text, images, or metadata, can provide a more comprehensive understanding of the text content and improve the accuracy of boundary detection models. By fusing data from different sources, the models can capture a broader range of features for more robust detection.

By incorporating these additional data properties and features into boundary detection models, researchers can develop more advanced and effective systems that can handle diverse real-world scenarios with higher accuracy and generalizability.

Given the challenges posed by semantic and grammar inconsistencies in simpler language models, how can detection systems be designed to better handle a diverse range of AI-generated content, including both high-quality and low-quality outputs?

To address the challenges posed by semantic and grammar inconsistencies in simpler language models and improve the handling of a diverse range of AI-generated content, including both high-quality and low-quality outputs, detection systems can be designed with the following strategies:

Ensemble Learning: Implementing ensemble learning techniques by combining multiple detection models can help in capturing a broader range of features and improving the overall performance of the system. By aggregating the predictions of diverse models, the system can achieve better accuracy and robustness in detecting boundaries between human-written and machine-generated text.

Fine-tuning on Diverse Data: Training detection models on a diverse dataset that includes high-quality and low-quality AI-generated content can help in improving the system's ability to differentiate between different text qualities. By exposing the models to a wide range of text variations, they can learn to identify inconsistencies in semantics and grammar more effectively.

Adversarial Training: Incorporating adversarial training techniques can enhance the system's resilience to adversarial examples, including text with semantic and grammar inconsistencies. By exposing the models to adversarial text samples during training, they can learn to detect and adapt to irregularities in the generated content.

Continuous Monitoring and Updating: Implementing a system for continuous monitoring and updating of the detection models can ensure that they remain effective in handling evolving AI-generated content. By regularly updating the models with new data and retraining them on the latest text samples, the system can adapt to changing patterns and maintain high performance levels.

By incorporating these strategies into the design of detection systems, researchers can develop more robust and adaptive solutions that can effectively handle a diverse range of AI-generated content, including both high-quality and low-quality outputs.