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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

papaer selecting

about 20 hours ago

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Paper • 2402.14083 • Published Feb 21 • 43
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 5
Training-Free Long-Context Scaling of Large Language Models

Paper • 2402.17463 • Published Feb 27 • 17
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 566

Metadata Might Make Language Models Better

Paper • 2211.10086 • Published Nov 18, 2022 • 3
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs

Paper • 2304.14999 • Published Apr 28, 2023 • 2
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

Paper • 2401.02122 • Published Jan 4 • 2
Zephyr: Direct Distillation of LM Alignment

Paper • 2310.16944 • Published Oct 25, 2023 • 116

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
Transforming and Combining Rewards for Aligning Large Language Models

Paper • 2402.00742 • Published Feb 1 • 10
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5 • 61
Specialized Language Models with Cheap Inference from Limited Domain Data

Paper • 2402.01093 • Published Feb 2 • 45

Inference Acceleration

BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models

Paper • 2401.12522 • Published Jan 23 • 11
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7 • 16
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Paper • 2402.04291 • Published Feb 6 • 48
Shortened LLaMA: A Simple Depth Pruning for Large Language Models

Paper • 2402.02834 • Published Feb 5 • 11

DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 173
Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models

Paper • 2401.04658 • Published Jan 9 • 24
Weaver: Foundation Models for Creative Writing

Paper • 2401.17268 • Published Jan 30 • 39
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15

Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws

Paper • 2401.00448 • Published Dec 31, 2023 • 25
Improving Text Embeddings with Large Language Models

Paper • 2401.00368 • Published Dec 31, 2023 • 72
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models

Paper • 2401.06951 • Published Jan 13 • 23
The Unreasonable Ineffectiveness of the Deeper Layers

Paper • 2403.17887 • Published Mar 26 • 75

Can LLMs Follow Simple Rules?

Paper • 2311.04235 • Published Nov 6, 2023 • 9
The Unreasonable Ineffectiveness of the Deeper Layers

Paper • 2403.17887 • Published Mar 26 • 75
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6 • 172
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Paper • 2402.17177 • Published Feb 27 • 87

llm_compression

BitNet: Scaling 1-bit Transformers for Large Language Models

Paper • 2310.11453 • Published Oct 17, 2023 • 94
Learning From Mistakes Makes LLM Better Reasoner

Paper • 2310.20689 • Published Oct 31, 2023 • 24
The Unreasonable Ineffectiveness of the Deeper Layers

Paper • 2403.17887 • Published Mar 26 • 75

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