85 citations · 139 across the 6 of their papers we have counts for
11 papers
Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification
Elena Merdjanovska, Omar Zaidan, Andreas Rücklé
Confidence estimation is essential when LLMs are used for classification, indicating when predictions can be trusted. However, common approaches such as verbalization produce extre…
Improving QA Generalization by Concurrent Modeling of Multiple Biases
Mingzhu Wu, Nafise Sadat Moosavi, Andreas Rücklé +1
Existing NLP datasets contain various biases that models can easily exploit to achieve high performances on the corresponding evaluation sets. However, focusing on dataset-specific…
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
Andreas Rücklé, Jonas Pfeiffer, Iryna Gurevych
We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums…
AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner +4
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortco…
AdapterHub: A Framework for Adapting Transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth +5
The current modus operandi in NLP involves downloading and fine-tuning pre-trained models consisting of millions or billions of parameters. Storing and sharing such large trained m…
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé +2
Sequential fine-tuning and multi-task learning are methods aiming to incorporate knowledge from multiple tasks; however, they suffer from catastrophic forgetting and difficulties i…
Neural Duplicate Question Detection without Labeled Training Data
Andreas Rücklé, Nafise Sadat Moosavi, Iryna Gurevych
Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obt…
Improving Generalization by Incorporating Coverage in Natural Language Inference
Nafise Sadat Moosavi, Prasetya Ajie Utama, Andreas Rücklé +1
The task of natural language inference (NLI) is to identify the relation between the given premise and hypothesis. While recent NLI models achieve very high performance on individu…
Pitfalls in the Evaluation of Sentence Embeddings
Steffen Eger, Andreas Rücklé, Iryna Gurevych
Deep learning models continuously break new records across different NLP tasks. At the same time, their success exposes weaknesses of model evaluation. Here, we compile several key…
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé +6
Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. W…
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Andreas Rücklé, Steffen Eger, Maxime Peyrard +1
Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models su…