DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Paper
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2309.03883
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Published
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14
random_papers
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LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
Agents: An Open-source Framework for Autonomous Language Agents
Paper • 2309.07870 • Published • 39 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 45 -
One Wide Feedforward is All You Need
Paper • 2309.01826 • Published • 31 -
Retentive Network: A Successor to Transformer for Large Language Models
Paper • 2307.08621 • Published • 167 -
Large Language Models as Optimizers
Paper • 2309.03409 • Published • 72 -
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 50 -
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Paper • 2309.09400 • Published • 77 -
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Paper • 2309.09958 • Published • 18 -
Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 37 -
Do Vision Transformers See Like Convolutional Neural Networks?
Paper • 2108.08810 • Published • 1 -
Neural Networks are Decision Trees
Paper • 2210.05189 • Published • 1 -
On the cross-validation bias due to unsupervised pre-processing
Paper • 1901.08974 • Published • 1 -
The Forward-Forward Algorithm: Some Preliminary Investigations
Paper • 2212.13345 • Published • 2 -
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets
Paper • 2201.02177 • Published • 1 -
DINOv2: Learning Robust Visual Features without Supervision
Paper • 2304.07193 • Published • 4 -
High-Resolution Image Synthesis with Latent Diffusion Models
Paper • 2112.10752 • Published • 7 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 7 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 11 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 7 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 9 -
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Paper • 2305.03495 • Published • 1 -
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Paper • 2301.13688 • Published • 8 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 11 -
Toolformer: Language Models Can Teach Themselves to Use Tools
Paper • 2302.04761 • Published • 10 -
RWKV: Reinventing RNNs for the Transformer Era
Paper • 2305.13048 • Published • 10 -
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
Paper • 2309.16058 • Published • 53 -
Context Tuning for Retrieval Augmented Generation
Paper • 2312.05708 • Published • 16 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 171 -
Exponentially Faster Language Modelling
Paper • 2311.10770 • Published • 117 -
Orca 2: Teaching Small Language Models How to Reason
Paper • 2311.11045 • Published • 69 -
LocalMamba: Visual State Space Model with Windowed Selective Scan
Paper • 2403.09338 • Published • 7