mlsanityData to 14 Oct 2026

GPT4All: An Ecosystem of Open Source Compressed Language Models

arXiv 2311.04931 ↗First posted 6 Nov 2023cs.CL cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

5 of 25 references.

References · 5

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  4. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,786
  5. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 2.

8 papers.