Automatic Detection of Machine Generated Text: A Critical Survey (2011.01314v1)

Abstract: Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both NLP and ML communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

Automatic Detection of Machine Generated Text: A Critical Survey

The paper "Automatic Detection of Machine Generated Text: A Critical Survey" provides a comprehensive survey of existing methods for detecting machine-generated text, a critical capability given the increasing capabilities of text generative models (TGMs) such as GPT-2 and GPT-3. While these models offer significant potential in numerous applications like story and report generation, they also present challenges, such as the automated generation of fake news and product reviews. The survey covers various detectors designed to distinguish machine-generated text from human-written text, emphasizing the need to develop accurate and efficient systems to combat the misuse of TGMs.

Core Contributions

The paper systematically categorizes existing detectors into four main types based on their underlying methodologies: classifiers trained from scratch, zero-shot classifiers, detectors involving fine-tuning of neural LLMs (NLMs), and human-machine collaborative approaches.

1. Classifiers Trained from Scratch: These include classical approaches, such as logistic regression using bag-of-words features, which can distinguish between human and machine text to an extent but struggle with complex samples where word order is critical. More advanced methods involve detecting the modeling choices of TGMs, which often leave detectable "artifacts" in generated text.
2. Zero-shot Classifiers: These methods utilize a pretrained TGM itself to detect its generated text. The GLTR tool, for instance, visually represents statistical anomalies in text probability distributions, helping humans and machines identify machine text.
3. Fine-tuned Neural LLMs: Fine-tuning existing NLMs, like RoBERTa, for the detection task has proven effective, often outperforming older methods, including fine-tuned versions of the generating TGMs themselves. However, these detectors are often data-hungry, requiring large training datasets to achieve high accuracy, and exhibit different weaknesses, such as difficulties with highly fluent or short text generated by TGMs.
4. Human-Machine Collaboration: Some approaches leverage the strengths of human perception, such as detecting semantic errors and contradictions better than automated systems alone. Tools that facilitate human participation in the detection process or evaluate the human detection capability, such as the GLTR and RoFT tools, show promise in increasing detection robustness.

Key Findings and Challenges

The state-of-the-art RoBERTa detector, while effective, is noted for requiring substantial training data to reach high accuracy, particularly in datasets like Amazon product reviews where text is often short and human-like. Moreover, error analysis of this detector reveals issues such as data inefficiency and susceptibility to errors related to fluency and factuality of text. These challenges highlight areas where improvement is necessary, particularly in building robust, data-efficient, and generalizable detection systems.

Implications and Future Directions

The survey proposes several future research directions crucial for advancing this field. These include utilizing auxiliary signals beyond text for better detection, assessing the veracity of text via cross-referencing with external sources, improving cross-domain generalizability, enhancing detector interpretability for human collaboration, and combating adversarial attacks. Robust detectors encompassing these advancements could play a pivotal role in mitigating potential threats, such as disinformation spread, while balancing the beneficial uses of TGMs.

Conclusion

This survey serves as a vital piece of literature that identifies significant progress and gaps in the detection of machine-generated text. It underscores the complexity and importance of developing detectors that are both sophisticated and adaptable, capable of addressing the evolving landscape of TGMs. By highlighting current challenges and proposing innovative future research pathways, it seeks to guide the community toward more efficient and reliable solutions. This will be essential as TGMs continue to advance and their integration into various domains expands.