607 citations · 1.2k across the 31 of their papers we have counts for
33 papers
iGSP:Implicit Gradient Subspace Projection for Efficient Continual Learning of Vision-Language Models
Xuezhi Cui, Dongbo Zhou, Wang Guo +8
Vision-Language Models require efficient adaptation to continually emerging downstream tasks. While Parameter-Efficient Fine-Tuning mitigates catastrophic forgetting, assigning iso…
Self-Alignment with Instruction Backtranslation
Xian Li, Ping Yu, Chunting Zhou +5
We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approac…
Large Language Model Programs
Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz +4
In recent years, large pre-trained language models (LLMs) have demonstrated the ability to follow instructions and perform novel tasks from a few examples. The possibility to param…
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model
Zeyu Leo Liu, Tim Dettmers, Xi Victoria Lin +2
Large and sparse feed-forward layers (S-FFN) such as Mixture-of-Experts (MoE) have proven effective in scaling up Transformers model size for \textit{pretraining} large language mo…
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru +15
Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few…
OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal +16
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computatio…
Multilingual Neural Machine Translation with Deep Encoder and Multiple Shallow Decoders
Xiang Kong, Adithya Renduchintala, James Cross +3
Recent work in multilingual translation advances translation quality surpassing bilingual baselines using deep transformer models with increased capacity. However, the extra latenc…
Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin +4
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this i…
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Badr AlKhamissi, Faisal Ladhak, Srini Iyer +7
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next. It…
Unified Speech-Text Pre-training for Speech Translation and Recognition
Yun Tang, Hongyu Gong, Ning Dong +8
We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four sel…
Efficient Language Modeling with Sparse all-MLP
Ping Yu, Mikel Artetxe, Myle Ott +4
All-MLP architectures have attracted increasing interest as an alternative to attention-based models. In NLP, recent work like gMLP shows that all-MLPs can match Transformers in la…
Efficient Large Scale Language Modeling with Mixtures of Experts
Mikel Artetxe, Shruti Bhosale, Naman Goyal +21
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive M…
Few-shot Learning with Multilingual Language Models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe +18
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these models are known to be able to jointly represent many different languages, their…
Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs
Peter Hase, Mona Diab, Asli Celikyilmaz +5
Do language models have beliefs about the world? Dennett (1995) famously argues that even thermostats have beliefs, on the view that a belief is simply an informational state decou…
Distributionally Robust Multilingual Machine Translation
Chunting Zhou, Daniel Levy, Xian Li +2
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of…
FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Task
Yun Tang, Hongyu Gong, Xian Li +4
In this paper, we describe our end-to-end multilingual speech translation system submitted to the IWSLT 2021 evaluation campaign on the Multilingual Speech Translation shared task.…
Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task
Yun Tang, Juan Pino, Xian Li +2
Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation…
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
Hongyu Gong, Yun Tang, Juan Pino +1
Multi-head attention has each of the attention heads collect salient information from different parts of an input sequence, making it a powerful mechanism for sequence modeling. Mu…
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation
Adithya Renduchintala, Denise Diaz, Kenneth Heafield +2
Is bias amplified when neural machine translation (NMT) models are optimized for speed and evaluated on generic test sets using BLEU? We investigate architectures and techniques co…
Robust Optimization for Multilingual Translation with Imbalanced Data
Xian Li, Hongyu Gong
Multilingual models are parameter-efficient and especially effective in improving low-resource languages by leveraging crosslingual transfer. Despite recent advance in massive mult…
Adaptive Sparse Transformer for Multilingual Translation
Hongyu Gong, Xian Li, Dmitriy Genzel
Multilingual machine translation has attracted much attention recently due to its support of knowledge transfer among languages and the low cost of training and deployment compared…
Improving Zero-Shot Translation by Disentangling Positional Information
Danni Liu, Jan Niehues, James Cross +2
Multilingual neural machine translation has shown the capability of directly translating between language pairs unseen in training, i.e. zero-shot translation. Despite being concep…
Deep Transformers with Latent Depth
Xian Li, Asa Cooper Stickland, Yuqing Tang +1
The Transformer model has achieved state-of-the-art performance in many sequence modeling tasks. However, how to leverage model capacity with large or variable depths is still an o…
Multilingual Speech Translation with Efficient Finetuning of Pretrained Models
Xian Li, Changhan Wang, Yun Tang +6
We present a simple yet effective approach to build multilingual speech-to-text (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder. Our…