RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Conclusion & Future Work
In this work, we address the problem of AI-generated text detection in the absence of labeled target data. We propose a contrastive domain adaptation framework that leverages the power of both unsupervised domain adaptation and self-supervised representation learning, in order to tackle the task of AI-generated text detection. Our experiments focus on news text, and show the effectiveness of the framework, as well as superior performance when compared to unsupervised baselines. We also perform a case study to evaluate our framework on our dataset of ChatGPT-generated news articles and achieve satisfactory performance. Our framework can be easily extended to other forms of text beyond news and our results suggest that such a framework may be effectively used for detection of AI-generated text when labels are unavailable, such as in the case of newly emerging generators. Future work can investigate more challenging variations of this problem, such as domain adaptation across multiple unlabeled target generators, generalization to fully unseen generators, etc., along with exploring other types of text such as scientific articles, medical literature, etc.
Problem Formulation & Model:
Despite the impressive performance of our ConDA model, there are several limitations that we go over in this section. First, our model and evaluations only focus on news text and performance may vary widely across other types of text such as creative writing, scientific articles, blog-style articles, etc. Second, our model simply tries to detect whether an input news article is generated by an LLM or not. AI generation does not necessarily imply malice. A dimension that our model does not consider is that of factuality: not all AI-generated news is factually inaccurate, and not all human-written news is factually correct. Incorporating factuality, perhaps in the form of a fact-checking module, could possibly improve the usefulness of our model. Third, our model, along with most other AI-generated text detectors, is not explainable. The discrete input space of natural language also makes it difficult to identify specific features that result in detection. Furthermore, given the black-box nature of LLMs, any detector that uses some LLM as the backbone, trade off explainability for performance gains.
ChatGPT Case Study:
As we elaborated in Section 4.1 , we create our own ChatGPT-generated news article dataset, following a procedure similar to Uchendu et al. ( 2021 ) . However, the data we generated is conditioned on the sample of human-written news articles we randomly selected. We suppose the performance of our model on this ChatGPT data hence is dependent on this sample. The high performance scores for ChatGPT-generated articles could also stem from the inherent structure of news articles; our data is specifically constrained to the style of journalistic news articles. Therefore, good performance on our news article dataset for ChatGPT does not necessarily imply similar performance across text from other areas. For this, more thorough evaluation is needed, which would be an interesting direction for future work.
Ethical Considerations
We go over some of the ethical considerations surrounding this work and similar directions.
Potential to Penalize Benign Use of LLMs
Recent articles have demonstrated how the newer language models including ChatGPT, GPT-4 OpenAI ( 2023 ) , Bing Chat 7 7 7 https://www.bing.com/new , etc. can be used to improve productivity, spur creative thinking, help with writing essays or cover letters or even explain concepts and help in homework. As these LLMs become more pervasive, standard use of these as writing or brain-storming assistants may become commonplace. In such a case, we may encounter an increasing amount of text generated by these LLMs online. Such text, if used for benign purposes such as the ones mentioned above, should not be penalized by a detector such as ours. This brings another dimension to this already challenging problem: the issue of intent. Flagging AI-generated content without characterizing the intent behind that could wrongfully penalize users of LLMs. Therefore, the nuances surrounding this need to be considered while using such a detector.
Danger of Misuse in High Stakes Areas
We discuss the issue of model misuse, by taking education as an example. Given the accessibility of ChatGPT and other recent AI-text generators, educators have expressed concerns Tlili et al. ( 2023 ) over students cheating or plagiarising via these new technologies. There are already commercial detectors for AI-generated content such as GPTZero 8 8 8 https://gptzero.me/ and one from Copyleaks 9 9 9 https://copyleaks.com/ai-content-detector that educators may use. However, similar to our model, there is always a margin of error on such detectors. Performing plagiarism checks and subsequently implementing punitive action based solely on such detectors may be detrimental in case of false positives. Legitimate work by a student may be misclassified by these detectors, and potentially impact their career. Eventually, this also diminishes trust in these detectors. Hence, before the widespread use of such AI-generated text detectors, thorough studies on error analysis and reliability need to be performed, along with policy changes to accommodate for the rapidly evolving landscape of AI technologies.
Acknowledgements
This work is supported by the DARPA SemaFor project (HR001120C0123), Office of Naval Research (N00014-
21-1-4002), Army Research Office (W911NF2110030) and Army Research Lab (W911NF2020124). The views, opinions and/or findings expressed are those of the authors.
A.1 Training Details & Hyper-parameters Used
We perform all experiments using PyTorch, on a single NVIDIA A100 GPU. We use an Adam optimizer with a learning rate of 2\times 10^{-5} . All models are trained for 5 epochs with early stopping, to avoid overfitting. We provide the full list of hyper-parameter values in Table 7 to facilitate reproducibility.

For values of \lambda_{1} and \lambda_{2} in Equation 3 , we use 0.5 and 1.0 respectively, after choosing these values from a small hyper-parameter search. We randomly choose 4 tasks: {F19 \rightarrow G3, C \rightarrow X, G2X \rightarrow GM, and G2X \rightarrow F19} and evaluate models with \lambda_{1}=\{0.2,0.5,0.8\} and \lambda_{2}=\{0.2,0.5,1.0\} . We finally choose \lambda_{1}=0.5 and \lambda_{2}=1.0 based on these evaluation performances.
A.2 Synonym Replacement Implementation
In order to implement the synonym replacement transformation, we perturb 10% of the words in each sentence in an article by replacing these with their synonyms. Out of these words, we only perform synonym replacement for words that have a NOUN , ADVERB , ADJECTIVE or VERB POS tag. Synonyms are based on WordNet Synsets from the nltk 10 10 10 https://www.nltk.org/ package. If a word has multiple synonyms, we choose one from that list, uniformly at random.
B.1 Labels
TuringBench Uchendu et al. ( 2021 ) has 200k samples across 20 labels. These labels include ‘human’ and 19 different generators, which are: { Human, GPT-1, GPT-2_small, GPT-2_medium, GPT-2_large, GPT-2_xl, GPT-2_PyTorch, GPT-3, GROVER_base, GROVER_large, GROVER_mega, CTRL, XLM, XLNET_base, XLNET_large, FAIR_wmt19, FAIR_wmt20, TRANSFORMER_XL, PPLM_distil, PPLM_gpt2}.
B.2 Human-written Articles
Human-written news articles in TuringBench are from The Washington Post, CNN, and a Kaggle dataset with CNN news articles from 2014-2020 and The Washington Post news articles from 2019-2020. More details on the TuringBench data are in Uchendu et al. ( 2021 ) . For the human-written articles in our ChatGPT News dataset, we use a random sample from the dataset of CNN and Washington Post articles as used in TuringBench.
Figure 1: Text embeddings from ( left ) source-only model and ( right ) ConDA model on target domain CTRL with GROVER_mega as source. Each domain has both ‘human’ and ‘AI’ text. ConDA effectively removes domain-specific features while retaining task-specific features, increasing the separability between ‘human’ and ‘AI’ text, and decreasing the separability between source and target domains.; Text embeddings from ( left ) source-only model and ( right ) ConDA model on target domain CTRL with GROVER_mega as source. Each domain has both ‘human’ and ‘AI’ text. ConDA effectively removes domain-specific features while retaining task-specific features, increasing the separability between ‘human’ and ‘AI’ text, and decreasing the separability between source and target domains.
Figure 2: Our ConDA framework. PLM refers to the pre-trained language model (here, RoBERTa); PLM and MLP weights are shared across all four instances.; Our ConDA framework. PLM refers to the pre-trained language model (here, RoBERTa); PLM and MLP weights are shared across all four instances.
(a) CTRL \rightarrow ChatGPT; (b) FAIR_wmt19 \rightarrow ChatGPT; (c) GPT-2_xl \rightarrow ChatGPT; (d) GPT-3 \rightarrow ChatGPT; (e) GROVER_mega \rightarrow ChatGPT; (f) XLM \rightarrow ChatGPT; t-SNE plots showing text representations from our ConDA model, for each of the S \rightarrow ChatGPT tasks, where S \in {CTRL, FAIR_wmt19, GPT-2_xl, GPT-3, GROVER_mega, XLM}, corresponding to plots ( a - f ), respectively.
List of generators we used for our evaluation.
Performance of ConDA on unlabeled target domains. Source-only model for each task S \rightarrow T refers to zero-shot evaluation of a model trained on S and evaluated on test set of T. \Delta F1 is increase (or decrease, in a few cases) in F1 scores of the ConDA model over the source-only model. Avg. scores in bold indicate where ConDA out-performs the source-only model.
Performance of our ConDA model on each of the target domains, with each of the other domains as source. Numbers in bold are the best performing ConDA models for each target domain, i.e. closest to fully supervised performance.
Performance of ConDA in comparison to unsupervised baselines, as AUROC. For ConDA, we report the average AUROC over all sources (for each target) and also the maximum AUROC (across all sources), along with the corresponding source in parentheses. Bold shows superior performance across each target.
Comparison of different model variants; bold shows best performance. We randomly chose 3 target domains to show in this table due to space constraints.
Results on our ChatGPT News dataset using unsupervised baselines (upper row) and ConDA (lower row). Scores are AUROC. Bold shows best and underline shows second best performance.
Hyper-parameter values we used for all our experiments.