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Collections including paper arxiv:2402.00858

Prompt Engineering - InContext Learning

The Prompt Report: A Systematic Survey of Prompting Techniques

Paper • 2406.06608 • Published Jun 6 • 52
In-Context Principle Learning from Mistakes

Paper • 2402.05403 • Published Feb 8 • 14
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 94
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 38
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

AI Paper of the Day

A collection of papers that I think are interesting, one added each day

about 5 hours ago

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
OLMo: Accelerating the Science of Language Models

Paper • 2402.00838 • Published Feb 1 • 78
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 140
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity

Paper • 2401.17072 • Published Jan 30 • 25

InContext Learning

In-Context Language Learning: Architectures and Algorithms

Paper • 2401.12973 • Published Jan 23 • 4
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
Transformers Can Achieve Length Generalization But Not Robustly

Paper • 2402.09371 • Published Feb 14 • 12

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
Specialized Language Models with Cheap Inference from Limited Domain Data

Paper • 2402.01093 • Published Feb 2 • 45
Premise Order Matters in Reasoning with Large Language Models

Paper • 2402.08939 • Published Feb 14 • 24

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 16
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Paper • 2401.15688 • Published Jan 28 • 11
SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Paper • 2401.15024 • Published Jan 26 • 67
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Paper • 2401.15071 • Published Jan 26 • 34

OLMo: Accelerating the Science of Language Models

Paper • 2402.00838 • Published Feb 1 • 78
Efficient Exploration for LLMs

Paper • 2402.00396 • Published Feb 1 • 21
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
Transforming and Combining Rewards for Aligning Large Language Models

Paper • 2402.00742 • Published Feb 1 • 11

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 16
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
The Impact of Reasoning Step Length on Large Language Models

Paper • 2401.04925 • Published Jan 10 • 15

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 52
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 19
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 6

ParadigmShift-Inquiry

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 34
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 21

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