AI "News" Content Farms Are Easy to Make and Hard to Detect: A Case Study in Italian

Large Language Models (LLMs) are increasingly used as "content farm" models (CFMs), to generate synthetic text that could pass for real news articles. This is already happening even for languages that do not have high-quality monolingual LLMs. We show that fine-tuning Llama (v1), mostly trained on English, on as little as 40K Italian news articles, is sufficient for producing news-like texts that native speakers of Italian struggle to identify as synthetic. We investigate three LLMs and three methods of detecting synthetic texts (log-likelihood, DetectGPT, and supervised classification), finding that they all perform better than human raters, but they are all impractical in the real world (requiring either access to token likelihood information or a large dataset of CFM texts). We also explore the possibility of creating a proxy CFM: an LLM fine-tuned on a similar dataset to one used by the real "content farm". We find that even a small amount of fine-tuning data suffices for creating a successful detector, but we need to know which base LLM is used, which is a major challenge. Our results suggest that there are currently no practical methods for detecting synthetic news-like texts 'in the wild', while generating them is too easy. We highlight the urgency of more NLP research on this problem.

SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

We present the results and the main findings of SemEval-2024 Task 8: Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection. The task featured three subtasks. Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. This subtask has two tracks: a monolingual track focused solely on English texts and a multilingual track. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine. The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30). In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For all subtasks, the best systems used LLMs.

The Invalsi Benchmark: measuring Language Models Mathematical and Language understanding in Italian

While Italian is by all metrics a high resource language, currently, there are isn't a Language Model pre-trained exclusively in this language. This results in a lower number of available benchmarks to evaluate the performance of language models in Italian. This work presents two new benchmarks to evaluate the models performance on mathematical understanding and language understanding in Italian. These benchmarks are based on real tests that are undertaken by students of age between 11 and 18 within the Italian school system and have therefore been validated by several experts in didactics and pedagogy. To validate this dataset we evaluate the performance of 9 language models that are the best performing when writing in Italian, including our own fine-tuned models. We show that this is a challenging benchmark where current language models are bound by 60\% accuracy. We believe that the release of this dataset paves the way for improving future models mathematical and language understanding in Italian.

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical in combating disinformation, preserving the integrity of education and scientific fields, and maintaining trust in communication. In this work, we address this problem by introducing a new benchmark involving multilingual, multi-domain and multi-generator for MGT detection -- M4GT-Bench. It is collected for three task formulations: (1) mono-lingual and multi-lingual binary MGT detection; (2) multi-way detection identifies which particular model generates the text; and (3) human-machine mixed text detection, where a word boundary delimiting MGT from human-written content should be determined. Human evaluation for Task 2 shows less than random guess performance, demonstrating the challenges to distinguish unique LLMs. Promising results always occur when training and test data distribute within the same domain or generators.

Outliers Dimensions that Disrupt Transformers Are Driven by Frequency

Transformer-based language models are known to display anisotropic behavior: the token embeddings are not homogeneously spread in space, but rather accumulate along certain directions. A related recent finding is the outlier phenomenon: the parameters in the final element of Transformer layers that consistently have unusual magnitude in the same dimension across the model, and significantly degrade its performance if disabled. We replicate the evidence for the outlier phenomenon and we link it to the geometry of the embedding space. Our main finding is that in both BERT and RoBERTa the token frequency, known to contribute to anisotropicity, also contributes to the outlier phenomenon. In its turn, the outlier phenomenon contributes to the "vertical" self-attention pattern that enables the model to focus on the special tokens. We also find that, surprisingly, the outlier effect on the model performance varies by layer, and that variance is also related to the correlation between outlier magnitude and encoded token frequency.