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Collections

Discover the best community collections!

Collections including paper arxiv:2404.05961

McGill-NLP/LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-supervised

Sentence Similarity • Updated Oct 8, 2024 • 137 • 4
McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised

Sentence Similarity • Updated Apr 30, 2024 • 11.6k • 48
McGill-NLP/LLM2Vec-Mistral-7B-Instruct-v2-mntp-supervised

Sentence Similarity • Updated Apr 11, 2024 • 975 • 13
McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised

Sentence Similarity • Updated Apr 11, 2024 • 332 • 3

Foundation AI Papers

Curated List of Must-Reads on LLM reasoning at Temus AI team

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Paper • 2310.04406 • Published Oct 6, 2023 • 10
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15, 2024 • 105
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14, 2024 • 6
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6, 2024 • 115

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65

Interesting Papers

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11, 2024 • 90
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65
Compression Represents Intelligence Linearly

Paper • 2404.09937 • Published Apr 15, 2024 • 27
Multi-Head Mixture-of-Experts

Paper • 2404.15045 • Published Apr 23, 2024 • 60

Decoder Upcycled to Embeddings

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65

Paper for LLM training

A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity

Paper • 2401.01967 • Published Jan 3, 2024
Secrets of RLHF in Large Language Models Part I: PPO

Paper • 2307.04964 • Published Jul 11, 2023 • 29
Zephyr: Direct Distillation of LM Alignment

Paper • 2310.16944 • Published Oct 25, 2023 • 123
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65

Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10, 2024 • 107
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65
OmniFusion Technical Report

Paper • 2404.06212 • Published Apr 9, 2024 • 75

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10, 2024 • 107

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Paper • 2404.05961 • Published Apr 9, 2024 • 65
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Paper • 2404.07143 • Published Apr 10, 2024 • 107
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies

Paper • 2404.08197 • Published Apr 12, 2024 • 29
Pre-training Small Base LMs with Fewer Tokens

Paper • 2404.08634 • Published Apr 12, 2024 • 35

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