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Found 7 papers, 5 papers with code
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards
Building on this dataset, we propose three experimental settings that simulate real-world scenarios where TDM triples are fully defined, partially defined, or undefined during leaderboard construction.
Systematic Task Exploration with LLMs: A Study in Citation Text Generation
Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks.
MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
The rapid dissemination of misinformation through online social networks poses a pressing issue with harmful consequences jeopardizing human health, public safety, democracy, and the economy; therefore, urgent action is required to address this problem.
Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers
FNet achieves competitive performances concerning the original transformer encoder model while accelerating training process by removing the computational burden of the attention mechanism.
Impact of Tokenization on Language Models: An Analysis for Turkish
Furthermore, we find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers.
Large-Scale Hate Speech Detection with Cross-Domain Transfer
The experimental results supported by statistical tests show that Transformer-based language models outperform conventional bag-of-words and neural models by at least 5% in English and 10% in Turkish for large-scale hate speech detection.
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
In other words, we align words that are already determined to be related, along predefined concepts.
