NLP Teams ; Deep NLP AND Prompts
Common descendants
16 Documents
[2404.11018] Many-Shot In-Context Learning
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2024-04-21 About
GitHub - microsoft/LLMLingua: To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
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2024-01-18 About
Zain Hasan sur X : "why do large language models pay more attention to and reason better over the beginning and end of what you tell them in prompts?"
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2023-11-19 About
LLM prompting guide
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2023-10-13 About
[2309.12307] LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
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2023-09-26 About
P-tuning for sequence classification
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2023-08-28 About
[2305.14128] Dr.ICL: Demonstration-Retrieved In-context Learning
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2023-07-14 About
LangChain Retrieval Webinar - YouTube
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2023-05-27 About
stanfordnlp/dspy: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.
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2023-02-18 About
Prompting, Instruction Finetuning, and RLHF (CS224N)
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2023-02-16 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
[2209.01975] Selective Annotation Makes Language Models Better Few-Shot Learners
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2022-09-07 About
[2203.08913] Memorizing Transformers
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2022-05-07 About
How many data points is a prompt worth?
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2021-04-08 About