# The Open Source Advantage in Large Language Models (LLMs)

arXiv: https://arxiv.org/abs/2412.12004 · HTML: https://mlsanity.com/paper/2412.12004/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 105 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 30 of 60)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [The Falcon Series of Open Language Models](https://mlsanity.com/paper/2311.16867/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus](https://mlsanity.com/paper/2104.08758/) (2021)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)

## Built on by (most cited first, 18 of 18)

- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks](https://mlsanity.com/paper/2505.16901/) (2025)
- [Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents](https://mlsanity.com/paper/2606.26080/) (2026)
- [Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks](https://mlsanity.com/paper/2601.18226/) (2026)
- [MeMo: Memory as a Model](https://mlsanity.com/paper/2605.15156/) (2026)
- [The Tower of Babel Revisited: Multilingual Jailbreak Prompts on Closed-Source Large Language Models](https://mlsanity.com/paper/2505.12287/) (2025)
- [An Open-Source Dual-Loss Embedding Model for Semantic Retrieval in Higher Education](https://mlsanity.com/paper/2505.04916/) (2025)
- [Large Language Models for IT Automation Tasks: Are We There Yet?](https://mlsanity.com/paper/2505.20505/) (2025)
- [Prompt Programming for Cultural Bias and Alignment of Large Language Models](https://mlsanity.com/paper/2603.16827/) (2026)
- [Federated Foundation Language Model Post-Training Should Focus on Open-Source Models](https://mlsanity.com/paper/2505.23593/) (2025)
- [LogiPart: Local Large Language Models for Data Exploration at Scale with Logical Partitioning](https://mlsanity.com/paper/2509.22211/) (2025)
- [Populism Meets AI: Advancing Populism Research with LLMs](https://mlsanity.com/paper/2510.07458/) (2025)
- [Matching Ranks Over Probability Yields Truly Deep Safety Alignment](https://mlsanity.com/paper/2512.05518/) (2025)
- [Chinese Labor Law Large Language Model Benchmark](https://mlsanity.com/paper/2601.09972/) (2026)
- [Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning](https://mlsanity.com/paper/2604.06385/) (2026)
- [Agentic Insight Generation in VSM Simulations](https://mlsanity.com/paper/2604.12421/) (2026)
- [Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach](https://mlsanity.com/paper/2607.24259/) (2026)
- [Model Retirement Creates Reproducibility Risk in Biomedical AI Publications](https://mlsanity.com/paper/2609.04699/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning. The field now faces a critical dilemma in its approach: closed-source models like GPT-4 deliver state-of-the-art performance but restrict reproducibility, accessibility, and external oversight, while open-source frameworks like LLaMA and Mixtral democratize access, foster collaboration, and support diverse applications, achieving competitive results through techniques like instruction tuning and LoRA. Hybrid approaches address challenges like bias mitigation and resource accessibility by combining the scalability of closed-source systems with the transparency and inclusivity of open-source framework. However, in this position paper, we argue that open-source remains the most robust path for advancing LLM research and ethical deployment.