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Found 8 papers, 4 papers with code
How Do BERT Embeddings Organize Linguistic Knowledge?
Several studies investigated the linguistic information implicitly encoded in Neural Language Models.
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy.
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025.
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.
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30).
The Invalsi Benchmarks: measuring Linguistic and Mathematical understanding of Large Language Models in Italian
For both Invalsi MATE and Invalsi ITA we compare LLMs with the average performance of Italian students to show that Llama 3. 1 is the only one to outperform them on Invalsi MATE while most models do so on Invalsi ITA, we then show that Olimpiadi MATE is more challenging than Invalsi MATE and the highest accuracy, achieved by Llama 3. 1 405b instruct is 45%.
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.
Outliers Dimensions that Disrupt Transformers Are Driven by Frequency
While Transformer-based language models are generally very robust to pruning, there is the recently discovered outlier phenomenon: disabling only 48 out of 110M parameters in BERT-base drops its performance by nearly 30% on MNLI.
