-
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 99 -
Large Language Models Cannot Self-Correct Reasoning Yet
Paper • 2310.01798 • Published • 33 -
Premise Order Matters in Reasoning with Large Language Models
Paper • 2402.08939 • Published • 25 -
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
Paper • 2402.12875 • Published • 13
Collections
Discover the best community collections!
Collections including paper arxiv:2310.01798
-
Measuring the Effects of Data Parallelism on Neural Network Training
Paper • 1811.03600 • Published • 2 -
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Paper • 1804.04235 • Published • 2 -
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Paper • 1905.11946 • Published • 3 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 62
-
Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 18 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46 -
How Far Are We from Intelligent Visual Deductive Reasoning?
Paper • 2403.04732 • Published • 18 -
Learning to Reason and Memorize with Self-Notes
Paper • 2305.00833 • Published • 4
-
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Paper • 2310.04484 • Published • 5 -
Diversity of Thought Improves Reasoning Abilities of Large Language Models
Paper • 2310.07088 • Published • 5 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 77 -
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
Paper • 2310.13332 • Published • 14
-
Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 2 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 24 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 47
-
BitNet: Scaling 1-bit Transformers for Large Language Models
Paper • 2310.11453 • Published • 96 -
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Paper • 2310.11511 • Published • 74 -
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 41 -
Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 40
-
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Paper • 2309.08532 • Published • 52 -
Contrastive Decoding Improves Reasoning in Large Language Models
Paper • 2309.09117 • Published • 37 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 77 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 82
-
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Paper • 2310.03214 • Published • 18 -
SteP: Stacked LLM Policies for Web Actions
Paper • 2310.03720 • Published • 7 -
Large Language Models Cannot Self-Correct Reasoning Yet
Paper • 2310.01798 • Published • 33 -
Mixtral of Experts
Paper • 2401.04088 • Published • 157