LogoLogo
Continuum WebsiteContinuum ApplicationsContinuum KnowledgeAxolotl Platform
Continuum Knowledge
Continuum Knowledge
  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
Powered by GitBook
LogoLogo

Continuum - Accelerated Artificial Intelligence

  • Continuum Website
  • Axolotl Platform

Copyright Continuum Labs - 2023

On this page
  • The Limitation of Current Instruction-Tuned Models
  • Introducing SELF-INSTRUCT: A Paradigm Shift
  • Best Practices for Creating Self-Instruct Datasets
  • Examples of Self-Instruct Datasets
  • Empirical Evidence of Performance Gains
  • Beyond Performance: Democratising Instruction-Based Fine-Tuning
  • A New Horizon for Research

Was this helpful?

  1. Data
  2. Datasets

Self Instruct Paper

The most highly cited paper on fine tuning methods

PreviousTypes of Fine TuningNextSelf-Alignment with Instruction Backtranslation

Last updated 10 months ago

Was this helpful?

Language models that are fine-tuned to follow human-written instructions have shown remarkable abilities in understanding and generating text.

However, they face limitations due to their dependence on a limited amount of human-written instruction data, which lacks diversity and creativity. These constraints hinder the model's ability to generalise across a wider range of tasks.

To address these limitations, this important May 2023 paper introduced the SELF-INSTRUCT framework.

This framework uses a bootstrapping approach, where the language model generates its own instruction, input, and output samples.

These generated samples are then refined and used to fine-tune the original model. This approach creates an almost annotation-free method for aligning pre-trained language models with instructions, overcoming the constraints posed by limited human-written instruction data.

The Limitation of Current Instruction-Tuned Models

At the core of traditional instruction-tuned models lies their dependency on human-written instructions. This dependency creates a bottleneck, limiting the quantity, diversity, and creativity of instruction data available for model training.

As a result, the models' ability to generalise and perform across a broad spectrum of tasks is constrained.

Introducing SELF-INSTRUCT: A Paradigm Shift

The SELF-INSTRUCT framework emerged as a solution to overcome the limitations of traditional instruction-tuned models.

At its heart, SELF-INSTRUCT employs a bootstrapping method that enables the language model to generate its own instruction, input, and output samples.

This approach not only minimises the need for human-annotated data but also introduces a higher level of diversity and creativity in the instruction data generated.

The generated samples are then pruned and used to fine-tune the original model, aligning it more closely with human-written instructions while significantly reducing the dependency on human-generated content.

Best Practices for Creating Self-Instruct Datasets

Creating effective self-instruct datasets involves a combination of strategic planning, iterative development, and diverse inputs. Here are some best practices to consider:

Diverse and Representative Seed Instructions

  • Goal: Ensure the initial seed instructions cover a broad spectrum of tasks across different domains to promote a wide-ranging dataset.

  • Example: Starting with seeds that include instructions for culinary recipes, technical troubleshooting, academic essay writing, and fitness exercise guides.

Iterative Refinement

  • Goal: Continuously improve the quality of the dataset by generating instructions, assessing their utility and clarity, and refining them based on feedback.

  • Example: Using an initial dataset to train a model, then using the model’s output to identify gaps and errors which guide the creation of revised instructions that better meet the needs of the tasks.

Quality Control Mechanisms

  • Goal: Implement processes to remove or revise instructions that are unclear, biased, or overly simplistic.

  • Example: Employing automated tools to detect and eliminate biased language or using manual reviews to ensure instructions meet a set of quality standards.

Balancing Novelty and Coherence

  • Goal: Create instructions that are both innovative and logical, ensuring they lead to executable tasks.

  • Example: Developing instructions for a new game or app functionality that are understandable and actionable by a broad user base.

Examples of Self-Instruct Datasets

To illustrate these best practices, here are examples of datasets created following the above guidelines:

Dataset for Common Daily Tasks

  • Instruction: Plan a daily schedule.

  • Input: List of tasks: Doctor's appointment at 9 AM, Team meeting at 11 AM, Gym at 3 PM, Grocery shopping at 5 PM.

  • Output: "Your daily schedule is: 9 AM - Doctor's appointment, 11 AM - Team meeting, 1 PM - Lunch break, 3 PM - Gym, 5 PM - Grocery shopping."

Dataset for Text Processing Tasks

  • Instruction: Summarise the following article.

  • Input: Text: "Climate change is increasingly seen as an urgent global issue, with rising temperatures affecting diverse ecosystems."

  • Output: "The article highlights the urgent need to address global warming and its impacts on ecosystems."

Dataset for Educational Content Creation

  • Instruction: Create a quiz question based on the given information.

  • Input: Topic: "Photosynthesis", Details: "Photosynthesis is the process by which plants convert sunlight into energy, using water and carbon dioxide, releasing oxygen as a byproduct."

  • Output: "What are the essential elements involved in photosynthesis and what byproduct does it release?"

Dataset for Code Generation Tasks

  • Instruction: Write a Python function to calculate the Body Mass Index (BMI).

  • Input: Null

  • Output:

def calculate_bmi(weight, height):
    return weight / (height ** 2)

Empirical Evidence of Performance Gains

The efficacy of SELF-INSTRUCT is not just theoretical. When applied to models like GPT-3, the framework has demonstrated substantial improvements.

Specifically, it achieved a 33% performance boost on the SUPERNATURALINSTRUCTIONS dataset, nearly matching the performance of InstructGPT, which benefits from private user data and human annotations.

Human evaluators have also confirmed that models fine-tuned with SELF-INSTRUCT surpass those tuned with existing public instruction datasets, marking a significant leap forward in model performance.

Beyond Performance: Democratising Instruction-Based Fine-Tuning

SELF-INSTRUCT's impact extends beyond performance metrics.

By minimising the reliance on human annotations, the framework democratises the process of instruction-based fine-tuning.

This is particularly important given the previously noted challenges with the scalability and generalisability of instruction-following models due to the reliance on human-annotated data.

SELF-INSTRUCT's approach also opens the door to exploring its application in commercial settings, particularly in automating or semi-automating the fine-tuning process for bespoke applications.

A New Horizon for Research

The introduction of SELF-INSTRUCT represented a shift in how we approach the fine-tuning of language models.

By automating the generation of diverse and creative instruction data, the framework addresses the critical bottlenecks of human annotation and the limitations of existing public datasets.

Furthermore, the SELF-INSTRUCT framework has potential applications in multi-modal learning, indicating its versatility and the broad implications of its use.

LogoSelf-Instruct: Aligning Language Models with Self-Generated InstructionsarXiv.org
Self-Instruct Paper
Page cover image