Collections

11

Textbooks Are All You Need

Paper • 2306.11644 • Published Jun 20, 2023 • 142
Textbooks Are All You Need II: phi-1.5 technical report

Paper • 2309.05463 • Published Sep 11, 2023 • 87
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Paper • 2305.07759 • Published May 12, 2023 • 34
Scaling Synthetic Data Creation with 1,000,000,000 Personas

Paper • 2406.20094 • Published Jun 28, 2024 • 97

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Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Paper • 2401.16380 • Published Jan 29, 2024 • 49
Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11, 2024 • 30
WizardLM: Empowering Large Language Models to Follow Complex Instructions

Paper • 2304.12244 • Published Apr 24, 2023 • 14
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Paper • 2402.13064 • Published Feb 20, 2024 • 48

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Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Paper • 2310.13961 • Published Oct 21, 2023 • 5

Textbooks Are All You Need

Textbooks Are All You Need II: phi-1.5 technical report

TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Scaling Synthetic Data Creation with 1,000,000,000 Personas

Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Best Practices and Lessons Learned on Synthetic Data for Language Models

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Understanding LLMs: A Comprehensive Overview from Training to Inference

Learning To Teach Large Language Models Logical Reasoning

ChipNeMo: Domain-Adapted LLMs for Chip Design

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Tuna: Instruction Tuning using Feedback from Large Language Models

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models

From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

The Consensus Game: Language Model Generation via Equilibrium Search

Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning

Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering

Tuna: Instruction Tuning using Feedback from Large Language Models

Retrieval-Generation Synergy Augmented Large Language Models

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Diversity of Thought Improves Reasoning Abilities of Large Language Models

AutoMix: Automatically Mixing Language Models

SAI: Solving AI Tasks with Systematic Artificial Intelligence in Communication Network

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs

Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models

Evaluating the Robustness to Instructions of Large Language Models

Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs

ZeroGen: Efficient Zero-shot Learning via Dataset Generation

Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models

Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs