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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 • 8
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 90
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

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

Paper • 2402.03620 • Published Feb 6 • 102

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28 • 9

Transformers are Multi-State RNNs

Paper • 2401.06104 • Published Jan 11 • 33
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
TinyLlama: An Open-Source Small Language Model

Paper • 2401.02385 • Published Jan 4 • 80

MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Paper • 2401.10774 • Published Jan 19 • 50
Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 135
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding

Paper • 2401.12954 • Published Jan 23 • 28

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

Paper • 2402.19427 • Published Feb 29 • 48
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 17
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 18
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 5

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 72
YAYI 2: Multilingual Open-Source Large Language Models

Paper • 2312.14862 • Published Dec 22, 2023 • 11

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Paper • 2401.06066 • Published Jan 11 • 35
Mixtral of Experts

Paper • 2401.04088 • Published Jan 8 • 152
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
LLM Augmented LLMs: Expanding Capabilities through Composition

Paper • 2401.02412 • Published Jan 4 • 35

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Paper • 2401.05566 • Published Jan 10 • 23
TrustLLM: Trustworthiness in Large Language Models

Paper • 2401.05561 • Published Jan 10 • 62
Zero Bubble Pipeline Parallelism

Paper • 2401.10241 • Published Nov 30, 2023 • 19

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44

new architecture

Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM

Paper • 2401.02994 • Published Jan 4 • 44
MambaByte: Token-free Selective State Space Model

Paper • 2401.13660 • Published Jan 24 • 47
Repeat After Me: Transformers are Better than State Space Models at Copying

Paper • 2402.01032 • Published Feb 1 • 22
BlackMamba: Mixture of Experts for State-Space Models

Paper • 2402.01771 • Published Feb 1 • 22

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