Transformers ; LLM AND Prompting/In-context learning
Common descendants
13 Documents
Albert Gu sur X : "Quadratic attention has been indispensable for information-dense modalities such as language... until now..."
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2023-12-05 About
Philipp Schmid sur X : “YaRN” allows you to scale LLMs like llama 2 to over 100k context!...
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2023-09-01 About
[2307.02486] LongNet: Scaling Transformers to 1,000,000,000 Tokens
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2023-07-06 About
[2306.07536] TART: A plug-and-play Transformer module for task-agnostic reasoning
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2023-06-15 About
Aran Komatsuzaki sur Twitter : "Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System Enables LLMs to outperform ChatGPT in scenarios involving ultra-long document summarization or long-term conversations."
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2023-04-27 About
Aran Komatsuzaki sur Twitter : "Scaling Transformer to 1M tokens and beyond with Recurrent Memory Transformer..."
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2023-04-25 About
Andrej Karpathy sur Twitter : "Next frontier of prompt engineering imo: "AutoGPTs"..."
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2023-04-02 About
Prompting, Instruction Finetuning, and RLHF (CS224N)
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2023-02-16 About
Riley Goodside sur Twitter : "OpenAI’s ChatGPT is susceptible to prompt injection — say the magic words, “Ignore previous directions”, and..."
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2022-12-18 About
[2208.01066] What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
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2022-09-17 About
[2201.04337] PromptBERT: Improving BERT Sentence Embeddings with Prompts
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2022-09-16 About
Prompt Tuning BERT🎯:CommonLit Readability | Kaggle
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2022-09-16 About
Prompting: Better Ways of Using Language Models for NLP Tasks
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2022-07-12 About