142 citations · 512 across the 30 of their papers we have counts for
14 papers · 1 filter
Text Quality-Based Pruning for Efficient Training of Language Models
Vasu Sharma, Karthik Padthe, Newsha Ardalani +8
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this pa…
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
Peifang Wang, Olga Golovneva, Armen Aghajanyan +4
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at a…
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan +1
Over recent years, an increasing amount of compute and data has been poured into training large language models (LLMs), usually by doing one-pass learning on as many tokens as poss…
Scaling Laws for Generative Mixed-Modal Language Models
Armen Aghajanyan, Lili Yu, Alexis Conneau +7
Generative language models define distributions over sequences of tokens that can represent essentially any combination of data modalities (e.g., any permutation of image tokens fr…
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer +1
Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood. We empirically study exact memorization in caus…
Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Singh Sachan, Mike Lewis, Mandar Joshi +4
We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question…
CM3: A Causal Masked Multimodal Model of the Internet
Armen Aghajanyan, Bernie Huang, Candace Ross +8
We introduce CM3, a family of causally masked generative models trained over a large corpus of structured multi-modal documents that can contain both text and image tokens. Our new…
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
Vivek Gupta, Akshat Shrivastava, Adithya Sagar +2
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory ha…
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Armen Aghajanyan, Dmytro Okhonko, Mike Lewis +4
We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provid…
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Arun Babu, Akshat Shrivastava, Armen Aghajanyan +3
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespr…
Muppet: Massive Multi-task Representations with Pre-Finetuning
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava +3
We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50…
Conversational Semantic Parsing
Armen Aghajanyan, Jean Maillard, Akshat Shrivastava +8
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the repr…
Pre-training via Paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh +3
We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the…
Towards Language Agnostic Universal Representations
Armen Aghajanyan, Xia Song, Saurabh Tiwary
When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in,even if the math…