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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
Collections
Discover the best community collections!
Collections trending this week
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Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 46 -
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 • 23 -
Matryoshka Representation Learning
Paper • 2205.13147 • Published • 30
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KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Paper • 2310.15511 • Published • 5 -
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models
Paper • 2310.14566 • Published • 27 -
SmartPlay : A Benchmark for LLMs as Intelligent Agents
Paper • 2310.01557 • Published • 13 -
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Paper • 2310.03214 • Published • 20
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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4
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Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 3 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 25 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 53
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TRAMS: Training-free Memory Selection for Long-range Language Modeling
Paper • 2310.15494 • Published • 2 -
A Long Way to Go: Investigating Length Correlations in RLHF
Paper • 2310.03716 • Published • 10 -
YaRN: Efficient Context Window Extension of Large Language Models
Paper • 2309.00071 • Published • 88 -
Giraffe: Adventures in Expanding Context Lengths in LLMs
Paper • 2308.10882 • Published • 1
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KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Paper • 2310.15511 • Published • 5 -
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Paper • 2310.13227 • Published • 15 -
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
Paper • 2310.04474 • Published • 2 -
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Paper • 2310.12823 • Published • 36
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In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 43 -
When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 3 -
Improving Length-Generalization in Transformers via Task Hinting
Paper • 2310.00726 • Published • 1 -
In-context Autoencoder for Context Compression in a Large Language Model
Paper • 2307.06945 • Published • 29
-
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4 -
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Paper • 2310.13127 • Published • 12 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1
-
Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
Paper • 2310.13961 • Published • 5 -
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Paper • 2202.07922 • Published • 1 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 19 -
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
Paper • 2309.09582 • Published • 4
-
Matryoshka Diffusion Models
Paper • 2310.15111 • Published • 46 -
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 • 23 -
Matryoshka Representation Learning
Paper • 2205.13147 • Published • 30
-
Moral Foundations of Large Language Models
Paper • 2310.15337 • Published • 1 -
Specific versus General Principles for Constitutional AI
Paper • 2310.13798 • Published • 3 -
Contrastive Prefence Learning: Learning from Human Feedback without RL
Paper • 2310.13639 • Published • 25 -
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Paper • 2309.00267 • Published • 53
-
TRAMS: Training-free Memory Selection for Long-range Language Modeling
Paper • 2310.15494 • Published • 2 -
A Long Way to Go: Investigating Length Correlations in RLHF
Paper • 2310.03716 • Published • 10 -
YaRN: Efficient Context Window Extension of Large Language Models
Paper • 2309.00071 • Published • 88 -
Giraffe: Adventures in Expanding Context Lengths in LLMs
Paper • 2308.10882 • Published • 1
-
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Paper • 2310.15511 • Published • 5 -
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models
Paper • 2310.14566 • Published • 27 -
SmartPlay : A Benchmark for LLMs as Intelligent Agents
Paper • 2310.01557 • Published • 13 -
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Paper • 2310.03214 • Published • 20
-
KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval
Paper • 2310.15511 • Published • 5 -
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Paper • 2310.13227 • Published • 15 -
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning
Paper • 2310.04474 • Published • 2 -
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Paper • 2310.12823 • Published • 36
-
In-Context Learning Creates Task Vectors
Paper • 2310.15916 • Published • 43 -
When can transformers reason with abstract symbols?
Paper • 2310.09753 • Published • 3 -
Improving Length-Generalization in Transformers via Task Hinting
Paper • 2310.00726 • Published • 1 -
In-context Autoencoder for Context Compression in a Large Language Model
Paper • 2307.06945 • Published • 29