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"A journey of a thousand miles begins with a single step." -Chinese Proverb

submission guidelines

UPDATED AUGUST 2, 2019

This tag is dedicated to beginners to programming, development, networking, or to a particular language. Everything should be geared towards that!

For Questions...

Consider using this tag along with #help, if...

You are new to a language, or to programming in general,
You want an explanation with NO prerequisite knowledge required.
You want insight from more experienced developers.

Please do not use this tag if you are merely new to a tool, library, or framework.

See also, #explainlikeimfive

For Articles...

Posts should be specifically geared towards true beginners (experience level 0-2 out of 10).
Posts should require NO prerequisite knowledge, except perhaps general (language-agnostic) essentials of programming.
Posts should NOT merely be for beginners to a tool, library, or framework.

If your article does not meet these qualifications, please select a different tag.

Promotional Rules

Posts should NOT primarily promote an external work. This is what Listings is for.
Otherwise accepable posts MAY include a brief (1-2 sentence) plug for another resource at the bottom.
Resource lists ARE acceptable if they follow these rules:
- Include at least 3 distinct authors/creators.
- Clearly indicate which resources are FREE, which require PII, and which cost money.
- Do not use personal affiliate links to monetize.
- Indicate at the top that the article contains promotional links.

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Surveilling the Masses with Wi-Fi-Based Positioning Systems

#machinelearning #ai #beginners #datascience

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Lessons from the Trenches on Reproducible Evaluation of Language Models

#machinelearning #ai #beginners #datascience

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Many-Shot In-Context Learning

#machinelearning #ai #beginners #datascience

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XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection

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Not All Language Model Features Are Linear

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MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

#machinelearning #ai #beginners #datascience

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LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70B

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Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks

#machinelearning #ai #beginners #datascience

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Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling

#machinelearning #ai #beginners #datascience

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The CAP Principle for LLM Serving

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Levels of AGI for Operationalizing Progress on the Path to AGI

#machinelearning #ai #beginners #datascience

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May 28 '24

ColorFoil: Investigating Color Blindness in Large Vision and Language Models

#machinelearning #ai #beginners #datascience

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May 28 '24

Increasing the LLM Accuracy for Question Answering: Ontologies to the Rescue!

#machinelearning #ai #beginners #datascience

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