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Collections including paper arxiv:2404.05961

Large Language Model (LLM) and NLP related papers.

LoRA+: Efficient Low Rank Adaptation of Large Models

Paper • 2402.12354 • Published Feb 19, 2024 • 6
The FinBen: An Holistic Financial Benchmark for Large Language Models

Paper • 2402.12659 • Published Feb 20, 2024 • 21
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

Paper • 2402.13249 • Published Feb 20, 2024 • 12
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10, 2024 • 69

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 • 105
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 • 105

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 • 105
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies

Paper • 2404.08197 • Published Apr 12, 2024 • 28
Pre-training Small Base LMs with Fewer Tokens

Paper • 2404.08634 • Published Apr 12, 2024 • 35

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
Adapting LLaMA Decoder to Vision Transformer

Paper • 2404.06773 • Published Apr 10, 2024 • 18
BRAVE: Broadening the visual encoding of vision-language models

Paper • 2404.07204 • Published Apr 10, 2024 • 19

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