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Joel Hestness

30 papers hereh-index 187.9k citations48 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author16
  • last author11

Across the 29 of 30 papers where every author was matched, so the position is known.

fields
  • cs.LG18
  • cs.CL7
  • cs.AI3
  • cs.SE1
  • eess.IV1
same name
  • Joel Hestness — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172026
most citedDeep Learning Scaling is Predictable, Empirically

424 citations · 505 across the 29 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023★ 1 cited

Position Interpolation Improves ALiBi Extrapolation

Faisal Al-Khateeb, Nolan Dey, Daria Soboleva +1

Linear position interpolation helps pre-trained models using rotary position embeddings (RoPE) to extrapolate to longer sequence lengths. We propose using linear position interpola…

cs.AI2023★ 2 cited

BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model

Nolan Dey, Daria Soboleva, Faisal Al-Khateeb +11

We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from th…

cs.CL2023★ 2 cited

SlimPajama-DC: Understanding Data Combinations for LLM Training

Zhiqiang Shen, Tianhua Tao, Liqun Ma +8

This paper aims to understand the impacts of various data combinations (e.g., web text, Wikipedia, GitHub, books) on the pretraining of large language models using SlimPajama. Slim…

cs.LG2023★ 24 cited

Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster

Nolan Dey, Gurpreet Gosal, Zhiming +6

We study recent research advances that improve large language models through efficient pre-training and scaling, and open datasets and tools. We combine these advances to introduce…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.