565 citations · 1.8k across the 38 of their papers we have counts for
11 papers · 1 filter
HyperTuning: Toward Adapting Large Language Models without Back-propagation
Jason Phang, Yi Mao, Pengcheng He +1
Fine-tuning large language models for different tasks can be costly and inefficient, and even methods that reduce the number of tuned parameters still require full gradient-based o…
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…
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions
Alicia Parrish, Harsh Trivedi, Nikita Nangia +4
The use of language-model-based question-answering systems to aid humans in completing difficult tasks is limited, in part, by the unreliability of the text these systems generate.…
EleutherAI: Going Beyond "Open Science" to "Science in the Open"
Jason Phang, Herbie Bradley, Leo Gao +2
Over the past two years, EleutherAI has established itself as a radically novel initiative aimed at both promoting open-source research and conducting research in a transparent, op…
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish +8
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field,…
Investigating Efficiently Extending Transformers for Long Input Summarization
Jason Phang, Yao Zhao, Peter J. Liu
While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
Alex Wang, Richard Yuanzhe Pang, Angelica Chen +2
Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains --…
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Sid Black, Stella Biderman, Eric Hallahan +14
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a…
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions
Alicia Parrish, Harsh Trivedi, Ethan Perez +4
Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot r…