2.2k citations · 3.1k across the 27 of their papers we have counts for
33 papers
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
Hao Peng, Qingqing Cao, Jesse Dodge +11
Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental co…
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi +8
In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on pa…
Just CHOP: Embarrassingly Simple LLM Compression
Ananya Harsh Jha, Tom Sherborne, Evan Pete Walsh +3
Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. A growing assortment of methods for compression pr…
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae +4
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains. However, applying continuous diffusion models to na…
The Semantic Scholar Open Data Platform
Rodney Kinney, Chloe Anastasiades, Russell Authur +45
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data pl…
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy +2
Although large language models can be prompted for both zero- and few-shot learning, performance drops significantly when no demonstrations are available. In this paper, we introdu…
What Language Model to Train if You Have One Million GPU Hours?
Teven Le Scao, Thomas Wang, Daniel Hesslow +16
The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across t…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
SciFact-Open: Towards open-domain scientific claim verification
David Wadden, Kyle Lo, Bailey Kuehl +4
While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in…
Continued Pretraining for Better Zero- and Few-Shot Promptability
Zhaofeng Wu, Robert L. Logan, Pete Walsh +4
Recently introduced language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. Neverthele…
Transparency Helps Reveal When Language Models Learn Meaning
Zhaofeng Wu, William Merrill, Hao Peng +2
Many current NLP systems are built from language models trained to optimize unsupervised objectives on large amounts of raw text. Under what conditions might such a procedure acqui…
What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow +5
Large pretrained Transformer language models have been shown to exhibit zero-shot generalization, i.e. they can perform a wide variety of tasks that they were not explicitly traine…
Staged Training for Transformer Language Models
Sheng Shen, Pete Walsh, Kurt Keutzer +3
The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training…
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Daniel King, Zejiang Shen, Nishant Subramani +3
Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between h…
Few-Shot Self-Rationalization with Natural Language Prompts
Ana Marasović, Iz Beltagy, Doug Downey +1
Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These model…
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
Wen Xiao, Iz Beltagy, Giuseppe Carenini +1
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts…
FLEX: Unifying Evaluation for Few-Shot NLP
Jonathan Bragg, Arman Cohan, Kyle Lo +1
Few-shot NLP research is highly active, yet conducted in disjoint research threads with evaluation suites that lack challenging-yet-realistic testing setups and fail to employ care…
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Rahul Nadkarni, David Wadden, Iz Beltagy +3
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applicatio…
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
Pradeep Dasigi, Kyle Lo, Iz Beltagy +3
Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the…
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
Arie Cattan, Sophie Johnson, Daniel Weld +4
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding. Previous work on cross-document coreference resolutio…
MS2: Multi-Document Summarization of Medical Studies
Jay DeYoung, Iz Beltagy, Madeleine van Zuylen +2
To assess the effectiveness of any medical intervention, researchers must conduct a time-intensive and highly manual literature review. NLP systems can help to automate or assist i…
CDLM: Cross-Document Language Modeling
Avi Caciularu, Arman Cohan, Iz Beltagy +3
We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective. First,…
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Arman Cohan, Sergey Feldman, Iz Beltagy +2
Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but t…
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta +4
Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether…