Ensemble-Instruct: Generating Instruction-Tuning Data with a Heterogeneous Mixture of LMs
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2310.13961
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Published
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4
Instruct
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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 • 11 -
Evaluating the Robustness to Instructions of Large Language Models
Paper • 2308.14306 • Published • 1 -
TeGit: Generating High-Quality Instruction-Tuning Data with Text-Grounded Task Design
Paper • 2309.05447 • Published • 1 -
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Paper • 2310.04484 • Published • 5 -
Toward Unified Controllable Text Generation via Regular Expression Instruction
Paper • 2309.10447 • Published • 1 -
Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model
Paper • 2310.08072 • Published • 1 -
Self-Alignment with Instruction Backtranslation
Paper • 2308.06259 • Published • 41 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 1 -
Multitask Prompted Training Enables Zero-Shot Task Generalization
Paper • 2110.08207 • Published • 2 -
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Paper • 2212.09689 • Published • 1 -
Tuna: Instruction Tuning using Feedback from Large Language Models
Paper • 2310.13385 • Published • 10 -
Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
Paper • 2309.08958 • Published • 2 -
CITING: Large Language Models Create Curriculum for Instruction Tuning
Paper • 2310.02527 • Published • 2 -
AlpaGasus: Training A Better Alpaca with Fewer Data
Paper • 2307.08701 • Published • 22 -
Autonomous Tree-search Ability of Large Language Models
Paper • 2310.10686 • Published • 2 -
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 • 35 -
FireAct: Toward Language Agent Fine-tuning
Paper • 2310.05915 • Published • 2 -
AutoMix: Automatically Mixing Language Models
Paper • 2310.12963 • Published • 14 -
Promptor: A Conversational and Autonomous Prompt Generation Agent for Intelligent Text Entry Techniques
Paper • 2310.08101 • Published • 2 -
You Only Look at Screens: Multimodal Chain-of-Action Agents
Paper • 2309.11436 • Published • 1 -
EcoAssistant: Using LLM Assistant More Affordably and Accurately
Paper • 2310.03046 • Published • 5 -
SALMON: Self-Alignment with Principle-Following Reward Models
Paper • 2310.05910 • Published • 2 -
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning
Paper • 2309.10814 • Published • 3 -
Improved Baselines with Visual Instruction Tuning
Paper • 2310.03744 • Published • 37 -
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Paper • 2308.09583 • Published • 7 -
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Paper • 2306.05425 • Published • 11 -
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
Paper • 2308.13437 • Published • 3 -
Ziya-VL: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning
Paper • 2310.08166 • Published • 1 -
Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations
Paper • 2308.14186 • Published • 1 -
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Paper • 2309.09958 • Published • 18 -
TextBind: Multi-turn Interleaved Multimodal Instruction-following
Paper • 2309.08637 • Published • 7 -
ImageBind-LLM: Multi-modality Instruction Tuning
Paper • 2309.03905 • Published • 16 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 14 -
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Paper • 2309.02301 • Published • 1 -
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Paper • 2309.04663 • Published • 5 -
SkyMath: Technical Report
Paper • 2310.16713 • Published • 2 -
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning
Paper • 2310.11716 • Published • 5 -
Improving Large Language Model Fine-tuning for Solving Math Problems
Paper • 2310.10047 • Published • 5 -
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
Paper • 2305.11159 • Published • 1 -
OctoPack: Instruction Tuning Code Large Language Models
Paper • 2308.07124 • Published • 28 -
Can Programming Languages Boost Each Other via Instruction Tuning?
Paper • 2308.16824 • Published • 9 -
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
Paper • 2308.12219 • Published • 1 -
Instruction Tuning for Large Language Models: A Survey
Paper • 2308.10792 • Published • 1 -
Zephyr: Direct Distillation of LM Alignment
Paper • 2310.16944 • Published • 122 -
Improving Translation Faithfulness of Large Language Models via Augmenting Instructions
Paper • 2308.12674 • Published • 1 -
Large Language Model as a User Simulator
Paper • 2308.11534 • Published • 2 -
OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
Paper • 2309.11235 • Published • 16 -
RA-DIT: Retrieval-Augmented Dual Instruction Tuning
Paper • 2310.01352 • Published • 7 -
XGen-7B Technical Report
Paper • 2309.03450 • Published • 8 -
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
Paper • 2308.16149 • Published • 25 -
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
Paper • 2310.07713 • Published • 3 -
Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
Paper • 2308.01240 • Published • 2 -
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Paper • 2306.12659 • Published • 1 -
InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
Paper • 2309.13064 • Published • 1 -
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Paper • 2306.04751 • Published • 5 -
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Paper • 2306.05087 • Published • 6 -
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Paper • 2310.03693 • Published • 1 -
Bactrian-X : A Multilingual Replicable Instruction-Following Model with Low-Rank Adaptation
Paper • 2305.15011 • Published • 1 -
InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning
Paper • 2305.13627 • Published • 1 -
M^3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
Paper • 2306.04387 • Published • 8 -
INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Paper • 2306.04757 • Published • 6 -
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning
Paper • 2310.00492 • Published • 2 -
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning
Paper • 2307.02053 • Published • 23 -
CoEdIT: Text Editing by Task-Specific Instruction Tuning
Paper • 2305.09857 • Published • 7 -
Scaling Instruction-Finetuned Language Models
Paper • 2210.11416 • Published • 7 -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 16 -
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
Paper • 2301.13688 • Published • 8 -
Chinese Open Instruction Generalist: A Preliminary Release
Paper • 2304.07987 • Published • 2 -
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
Paper • 2307.16039 • Published • 4 -
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Paper • 2308.12032 • Published • 1 -
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
Paper • 2306.05443 • Published • 3 -
SAIL: Search-Augmented Instruction Learning
Paper • 2305.15225 • Published • 2 -
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
Paper • 2305.18752 • Published • 3 -
Exploring Format Consistency for Instruction Tuning
Paper • 2307.15504 • Published • 7 -
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
Paper • 2308.08239 • Published • 1 -
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Paper • 2212.08061 • Published • 1 -
MUTEX: Learning Unified Policies from Multimodal Task Specifications
Paper • 2309.14320 • Published • 1 -
Towards Generating Functionally Correct Code Edits from Natural Language Issue Descriptions
Paper • 2304.03816 • Published • 1 -
Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering
Paper • 2304.07840 • Published • 1 -
ToolCoder: Teach Code Generation Models to use API search tools
Paper • 2305.04032 • Published • 1 -
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Paper • 2311.02303 • Published • 4 -
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Paper • 2306.08568 • Published • 28 -
WizardLM: Empowering Large Language Models to Follow Complex Instructions
Paper • 2304.12244 • Published • 13 -
ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation
Paper • 2311.00272 • Published • 9 -
Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks
Paper • 2310.10508 • Published • 1 -
Towards Anytime Fine-tuning: Continually Pre-trained Language Models with Hypernetwork Prompt
Paper • 2310.13024 • Published • 1 -
Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey
Paper • 2308.08234 • Published • 1 -
Understanding and Improving Information Transfer in Multi-Task Learning
Paper • 2005.00944 • Published • 1 -
Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning
Paper • 2303.10475 • Published • 2 -
InstructCoder: Empowering Language Models for Code Editing
Paper • 2310.20329 • Published • 2 -
GraphGPT: Graph Instruction Tuning for Large Language Models
Paper • 2310.13023 • Published • 2 -
LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models
Paper • 2308.10252 • Published • 1 -
Language Models can be Logical Solvers
Paper • 2311.06158 • Published • 18 -
When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dust
Paper • 2305.07230 • Published • 1 -
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models
Paper • 2305.07766 • Published • 1 -
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Paper • 2111.04130 • Published • 1 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 39 -
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Paper • 2308.12711 • Published • 1 -
Instruction Tuned Models are Quick Learners
Paper • 2306.05539 • Published • 1 -
Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
Paper • 2305.09246 • Published • 1 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 49 -
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Paper • 2312.14327 • Published • 6 -
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Paper • 2312.14878 • Published • 13 -
Self-Instruct: Aligning Language Model with Self Generated Instructions
Paper • 2212.10560 • Published • 8 -
ICE-GRT: Instruction Context Enhancement by Generative Reinforcement based Transformers
Paper • 2401.02072 • Published • 9 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 184 -
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Paper • 2311.07919 • Published • 9 -
Differentiable Instruction Optimization for Cross-Task Generalization
Paper • 2306.10098 • Published • 1 -
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Paper • 2401.01335 • Published • 64 -
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
Paper • 2209.09900 • Published • 1 -
ChatQA: Building GPT-4 Level Conversational QA Models
Paper • 2401.10225 • Published • 34 -
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
Paper • 2212.12017 • Published • 1 -
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions
Paper • 2305.15083 • Published • 2 -
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
Paper • 2401.02731 • Published • 2 -
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
Paper • 2401.01854 • Published • 10 -
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
Paper • 2401.00788 • Published • 21 -
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Paper • 2402.06619 • Published • 54 -
SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning
Paper • 2401.07950 • Published • 4 -
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Paper • 2403.18058 • Published • 4 -
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Paper • 2402.11532 • Published -
Enhancing Amharic-LLaMA: Integrating Task Specific and Generative Datasets
Paper • 2402.08015 • Published -
Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Paper • 2402.11597 • Published • 1 -
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
Paper • 2407.03040 • Published -
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity
Paper • 2405.16579 • Published