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AI@Google - Andrej Karpathy - Annotation tools - API - Artificial general intelligence - Arxiv Doc - Assistants - Conversational AI - Discuté avec Raphaël - Education - Emergent behaviours (AI) - Few-shot learning - Fine-tuning - Foundation Models - Generative AI - GitHub project - Google - Human-AI collaboration - Information retrieval - Language Models: size - Language Models + Knowledge - LM + Search - Médecine + IA - Microsoft Research - Multimodal Models - NLP@Google - NLP@Meta - NLP@Stanford - NLU - OpenAI - OpenAI API - Open Source - Personal Knowledge Management - Pre-Trained Language Models - Programming - Raphaël Sourty - RL from Human Feedback - Robotique - Semantic Search - Stephen Wolfram - Text Embeddings - Transfer learning - Transformers - Tutorial - Tweet - Unsupervised deep pre-training - Yann LeCun - YouTube video -

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Language Model

LLM

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Language Models: size

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18 Documents (Long List)

Jim Fan sur Twitter : "...NVIDIA AI Foundations "

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2023-03-24 About

Andrej Karpathy sur Twitter : "Base LLMs (non-finetuned) make very strong few-shot classifiers. Describe task in English, give few examples, read off the label probabilities on test example. No gradient-based optimization necessary. It brings a cannon to a knife fight but is fast, convenient, strong baseline." / Twitter

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2023-03-19 About

Alpaca: A Strong Open-Source Instruction-Following Model

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2023-03-14 About

Sergey Levine sur Twitter : "What if we train a language model on images & robot data? That's the idea behind PaLM-E: a huge LLM (562B params) that is trained on language and "multimodal sentences" that include images and language"

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2023-03-08 About

Jerry Liu sur Twitter : "A key goal of @gpt_index is to enable end users to ask an LLM *any* questions over their own data..."

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2023-02-27 About

LLM Powered Assistants for Complex Interfaces - Nick Arner

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2023-02-26 About

abacaj/awesome-transformers: A curated list of awesome transformer models.

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2023-02-25 About

Guillaume Lample sur Twitter : "Today we release LLaMA, 4 foundation models ranging from 7B to 65B parameters..."

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2023-02-25 About

Maria Khalusova @maria@recsys.social sur Twitter : "Did you know that you can tweak the text output generated by a LLM without changing any of the trainable parameters?..."

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2023-02-23 About

[2302.08091] Do We Still Need Clinical Language Models?

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2023-02-17 About

[2203.14465] STaR: Bootstrapping Reasoning With Reasoning

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2023-02-07 About

Yann LeCun sur Twitter : "On the highway towards Human-Level AI, Large Language Model is an off-ramp."

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2023-02-05 About

deepset sur Twitter : " Generative models have taken the world of NLP by storm. But LLMs do not know about your personal data. This makes personal assistants, enterprise knowledge management and many other applications challenging. Retrieval augmented pipelines are the answer"

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2023-02-02 About

hwchase17/langchain: ⚡ Building applications with LLMs through composability ⚡

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LLM

2023-02-02 About

The Flan Collection: Advancing open source methods for instruction tuning – Google AI Blog

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2023-02-02 About

An empirical analysis of compute-optimal large language model training

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2023-01-26 About

Andrej Karpathy sur Twitter : "Great post (5mo ago) "chinchilla's wild implications" giving context to LLM goldrush shifting from model size to dataset size..."

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2023-01-05 About

Some remarks on Large Language Models

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> There turned out to be a phase shift somewhere between 60B parameters and 175B parameters, that made language models super impressive.

> **The performance of current days language models are not obtained by language modeling**
>
>    - [Traditional] LMs are not [grounded](tag:grounded_language_learning)
> 
> **3 conceptual steps between GPT-3 and chatGPT: Instructions, code, RLHF.** The last one is, I think, the least interesting despite getting the most attention
>
> Instruction tuning: For example, the human annotators would write something like "please summarize this text", followed by some text they got, followed by a summary they produced of this text. -> Some symbols ("summarize", "translate", "formal") are used in a consistent way together with the concept/task they denote. And they always appear in the beginning of the text. -> the act of producing a summary grounded to the human concept of "summary"
>
> code: programming language code data, and specifically data that contains both natural language instructions or descriptions (in the form of code comments) and the corresponding programming language code. This produced another very direct form of grounding. the human language describes concepts (or intents), which are then realized in the form of the corresponding programs.
>
> "[RL with Human Feedback](tag:reinforcement_learning_from_human_feedback)". This is a fancy way of saying that the model now observes two humans in a conversation, one playing the role of a user, and another playing the role of "the AI", demonstrating how the AI should respond in different situations. This clearly helps the model learn how dialogs work, and how to keep track of information across dialog states (something that is very hard to learn from just "found" data).

2023-01-03 About

Properties

sl:creationDate : 2023-01-28
sl:creationTime : 2023-01-28T20:14:05Z
rdf:type : sl:Tag
skos:altLabel :
- Large Language Models
- Large Language Model
- LLMs
skos:prefLabel : LLM