3.1k citations · 4.1k across the 10 of their papers we have counts for
11 papers
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak +5
The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has paralle…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Prasetya Ajie Utama, Nafise Sadat Moosavi, Victor Sanh +1
Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling probl…
Low-Complexity Probing via Finding Subnetworks
Steven Cao, Victor Sanh, Alexander M. Rush
The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations. Th…
Learning from others' mistakes: Avoiding dataset biases without modeling them
Victor Sanh, Thomas Wolf, Yonatan Belinkov +1
State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying…
Movement Pruning: Adaptive Sparsity by Fine-Tuning
Victor Sanh, Thomas Wolf, Alexander M. Rush
Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become stand…
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh +19
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building hig…
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond +1
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under const…
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Thomas Wolf, Victor Sanh, Julien Chaumond +1
We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme an…
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
Victor Sanh, Thomas Wolf, Sebastian Ruder
Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) do…