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10 Benefits and 10 Challenges of Applying Large Language Models to DoD Software Acquisition | CMU Software Engineering Institute

insights.sei.cmu.edu · 10 min read

A post on insights.sei.cmu.edu is cited by 1 distinct blog to date (corpus.blog, measured 12 October 2026).

A record is what was published and who pointed at it. The text of the post is not shown here: read it at the source, or at its Wayback capture.

Read at insights.sei.cmu.edu (opens in a new tab) Wayback capture (opens in a new tab)

1blog citing

Citing blogs and links by period
Period Blogs Links
All time11
Last 90 days00
Last 30 days00
Last 7 days00
Figures for this page
First cited30 Jan 2024
Latest citation30 Jan 2024
Words blogs link it with1
External links25
Length2,112

Words blogs link it with

The words blogs link this page with, by how many different blogs use them.

  • 10 benefits and 10 challenges of applying large language models to dod software acquisition1 blog

Cited by 1 blog in 1 post

One row per blog, the most recently citing first. Each shows the blog's newest citing post; open a row for the others.

Blog
Newest citing post
Date
seroter.com1 post
Daily Reading List – January 29, 2024 (#249)

linked as “10 Benefits and 10 Challenges of Applying Large Language Models to DoD Software Acquisition”

Read at seroter.com (opens in a new tab)
30 Jan 2024

Links from this post

Link
Host
https://www.sei.cmu.edu/acquisition-transformation/ Read at sei.cmu.edu (opens in a new tab)
sei.cmu.edu
https://acqnotes.com/acqnote/careerfields/request-for-information
acqnotes.com
https://en.wikipedia.org/wiki/Generative_artificial_intelligence Who cites this page
en.wikipedia.org
https://en.wikipedia.org/wiki/Large_language_model Who cites this page
en.wikipedia.org
https://en.wikipedia.org/wiki/GPT-4 Who cites this page
en.wikipedia.org
https://oversight.house.gov/wp-content/uploads/2024/01/Scherlis-Testimony.pdf Who cites this page
oversight.house.gov
https://arxiv.org/abs/2303.07839 Who cites this page
arxiv.org
https://arxiv.org/abs/2302.11382 Who cites this page
arxiv.org
https://www.promptingguide.ai/introduction/settings?ref=aarondevelops.com Who cites this page
promptingguide.ai
https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning Who cites this page
en.wikipedia.org
https://en.wikipedia.org/wiki/Prompt_engineering Who cites this page
en.wikipedia.org
https://arxiv.org/abs/2309.05922 Who cites this page
arxiv.org
https://towardsdatascience.com/llm-monitoring-and-observability-c28121e75c2f Who cites this page
towardsdatascience.com
https://www.forbes.com/sites/forbestechcouncil/2023/11/06/how-to-mitigate-the-enterprise-security-risks-of-llms/?sh=712082006e14 Who cites this page
forbes.com
Privacy in the age of generative AI Read at stackoverflow.blog (opens in a new tab)
stackoverflow.blog
Bias and Fairness in Large Language Models: A Survey Read at montrealethics.ai (opens in a new tab)
montrealethics.ai
https://www.ncsi.com/event/cdao/tflima/
ncsi.com
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf Who cites this page
nvlpubs.nist.gov
What is retrieval-augmented generation? Read at research.ibm.com (opens in a new tab)
research.ibm.com
https://en.wikipedia.org/wiki/Adversarial_machine_learning Who cites this page
en.wikipedia.org
https://asc.army.mil/web/news-chatgpt-in-dod-acquisitions/
asc.army.mil
https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ Read at whitehouse.gov (opens in a new tab)
whitehouse.gov
https://www.gsa.gov/directives-library/security-policy-for-generative-artificial-intelligence-ai-large-language-models-llms Who cites this page
gsa.gov
https://www.defense.gov/News/Releases/Release/Article/3489803/dod-announces-establishment-of-generative-ai-task-force/ Who cites this page
defense.gov
https://www.doncio.navy.mil/ContentView.aspx?id=16442
doncio.navy.mil
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