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Collections including paper arxiv:2309.08968
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Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Paper • 2402.14083 • Published • 47 -
Linear Transformers are Versatile In-Context Learners
Paper • 2402.14180 • Published • 6 -
Training-Free Long-Context Scaling of Large Language Models
Paper • 2402.17463 • Published • 20 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 609
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Detecting Pretraining Data from Large Language Models
Paper • 2310.16789 • Published • 11 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19 -
AutoMix: Automatically Mixing Language Models
Paper • 2310.12963 • Published • 14 -
An Emulator for Fine-Tuning Large Language Models using Small Language Models
Paper • 2310.12962 • Published • 14
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Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time
Paper • 2310.17157 • Published • 13 -
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers
Paper • 2305.15805 • Published • 1 -
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Paper • 2305.11186 • Published • 1 -
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Paper • 2110.07560 • Published • 1
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AutoMix: Automatically Mixing Language Models
Paper • 2310.12963 • Published • 14 -
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
Paper • 2310.03094 • Published • 12 -
MatFormer: Nested Transformer for Elastic Inference
Paper • 2310.07707 • Published • 1 -
DistillSpec: Improving Speculative Decoding via Knowledge Distillation
Paper • 2310.08461 • Published • 1
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Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 42 -
SortedNet, a Place for Every Network and Every Network in its Place: Towards a Generalized Solution for Training Many-in-One Neural Networks
Paper • 2309.00255 • Published • 1 -
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT)
Paper • 2309.08968 • Published • 22 -
Matryoshka Representation Learning
Paper • 2205.13147 • Published • 11
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Large Content And Behavior Models To Understand, Simulate, And Optimize Content And Behavior
Paper • 2309.00359 • Published • 21 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 75 -
Natural Language Supervision for General-Purpose Audio Representations
Paper • 2309.05767 • Published • 9 -
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT)
Paper • 2309.08968 • Published • 22
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Language Modeling Is Compression
Paper • 2309.10668 • Published • 83 -
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference Using Sorted Fine-Tuning (SoFT)
Paper • 2309.08968 • Published • 22 -
Vision Transformers Need Registers
Paper • 2309.16588 • Published • 78 -
Localizing and Editing Knowledge in Text-to-Image Generative Models
Paper • 2310.13730 • Published • 7