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  • 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
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Copyright Continuum Labs - 2023

On this page
  • Overview of MTEB
  • Key Observations and Findings
  • Challenges in the Field
  • Contributions of the Paper
  • Benchmarks
  • Embedding Models
  • Additional MTEB Tasks
  • Specific Insights on Model Categories
  • Analysis of Task-Specific Performance
  • Multilingual Performance
  • Key Insights
  • Creative Ideas for New Embedding Tools
  • Current Best Embedding Tools for Different Tasks (Based on MTEB)

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  1. KNOWLEDGE
  2. Vector Databases

Massive Text Embedding Benchmark

The leaderboard for embedding models

PreviousImproving Text Embeddings with Large Language ModelsNextRocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

Last updated 11 months ago

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This March 2023 paper discusses the creation of the Massive Text Embedding Benchmark (MTEB).

Overview of MTEB

MTEB is introduced to address a gap in the evaluation of text embeddings.

The key points about MTEB are:

Scope of Evaluation

MTEB expands the evaluation of text embeddings beyond a narrow focus.

Traditional evaluations often limit themselves to a small set of datasets from a single task, not adequately covering the diverse applications of text embeddings.

Comprehensive Benchmarking

The benchmark encompasses 8 embedding tasks, including bitext mining, classification, clustering, pair classification, reranking, retrieval, semantic textual similarity (STS), and summarization, across 58 datasets and 112 languages.

Inclusiveness of Models

It benchmarks 33 different models, making it one of the most comprehensive benchmarks to date in the field of text embeddings.

Key Observations and Findings

Lack of Dominant Method

The evaluation reveals that no single text embedding method consistently outperforms others across all tasks. This suggests the absence of a universal text embedding method that can provide state-of-the-art results for all embedding tasks.

Diversity of Use Cases

The paper highlights the vast range of use cases for natural language embeddings, from clustering and topic representation to search systems and text mining, and as features for downstream models.

Practicality and Intractability

The paper notes the infeasibility of using generative language models or cross-encoders for certain applications due to their extensive computational requirements.

Challenges in the Field

Limited Evaluation Regimes

Current text embedding models are often evaluated in a constrained manner, focusing on tasks like STS and classification, but not thoroughly tested for transferability to other tasks like search or clustering.

Poor Correlation with Real-World Use Cases:

It's mentioned that STS evaluations may not correlate well with other real-world applications, indicating a gap in the current evaluation methodologies.

Influence of Implementation Details

The paper emphasises the impact that pre-processing and hyperparameter settings can have on model performance, suggesting that these factors can obscure whether performance improvements are genuine or a result of favourable evaluation setups.

Contributions of the Paper

Introduction of MTEB: The paper introduces MTEB as a solution to provide clarity on model performance across a variety of embedding tasks.

Ease of Evaluation: MTEB's open-source software allows for easy evaluation of any embedding model with minimal coding effort.

Holistic View: The paper promises a holistic view of the state of text embedding models, including both open-source models and those accessible via APIs.

No Single Best Solution: An important finding is that there is no single best solution for text embeddings, as different models excel in different tasks.

Benchmarks

Existing Benchmarks: The paper references various benchmarks like (Super)GLUE, Big-BENCH, and SemEval, which have traditionally been used for text embedding evaluation.

Limitations: These benchmarks have limitations, particularly in representing the variety of real-world applications. For instance, SemEval focuses mostly on semantic textual similarity (STS), while SentEval lacks tasks like retrieval or clustering. USEB is mentioned as primarily reranking-focused and BEIR as the standard for zero-shot information retrieval.

Insufficiency of STS: The document highlights the insufficiency of STS-focused benchmarks in capturing the broader spectrum of text embedding applications.

Embedding Models

Evolution of Models: The transition from context-unaware models like Glove to context-aware models based on the transformer architecture (like BERT and SBERT) is outlined.

Fine-Tuning: The paper mentions the trend of fine-tuning transformer models with a contrastive loss objective for text pair embeddings.

Variety and Confusion: There's an emphasis on the variety of pre-trained transformer models available, leading to confusion about which model is best for specific embedding use cases.

Additional MTEB Tasks

Retrieval: Involves identifying relevant documents for given queries. The model embeds queries and documents, and rankings are based on cosine similarity scores. Metrics like nDCG@k and MRR@k are used, with nDCG@10 being the primary metric.

Normalized Discounted Cumulative Gain (NDCG)

Normalized Discounted Cumulative Gain (NDCG) is a popular metric used to evaluate the performance of ranking models, particularly in search engines and recommendation systems.

Ranking models predict the ranks of items based on search queries and assign relevance scores to each item.

NDCG is a measure of ranking quality that compares the relevance of items returned by a search engine or recommendation system to an ideal ranking.

The components of NDCG are:

  1. Cumulative Gain (CG): The sum of relevance scores (gains) for items within a search query

  1. Discounted Cumulative Gain (DCG): Extends CG by discounting the gains based on the item's position in the ranking.

  1. Ideal Discounted Cumulative Gain (IDCG): The best possible DCG for a group, assuming the most relevant items are at the top.

  1. Normalized Discounted Cumulative Gain (NDCG): Normalizes DCG by dividing it by IDCG, allowing for fair comparisons between different search groups.

NDCG@K considers only the top K ranked items in the calculation.

NDCG is used in model monitoring to evaluate the performance of ranking models in production. They provide examples of how companies like music streaming apps and social media platforms use NDCG to assess the relevance of their recommendations.

A low NDCG value in production means and how it can indicate performance degradation in a recommendation system.

Semantic Textual Similarity (STS): The task is to determine the similarity of sentence pairs. The similarity is computed using distance metrics, benchmarked against ground truth similarities. Spearman correlation based on cosine similarity is the main metric.

Summarisation: Involves scoring machine-generated summaries against human-written ones. The closest score based on cosine similarity is used as the model’s score. Pearson and Spearman correlations with human assessments are the key metrics.

Specific Insights on Model Categories

Self-supervised Methods

  • Transformer-based: BERT, when used with mean-pooling, directly produces text embeddings. SimCSE-Unsup further enhances BERT with additional self-supervised training.

  • Non-transformer: Models like Komninos and Glove provide faster, context-unaware word embeddings.

Supervised Methods

  • Transformer Encoder Methods: Include models like coCondenser, Contriever, LaBSE, and SimCSE-BERT-sup, which are BERT-based with variations in training stages or data.

  • Transformer Decoder Methods: SGPT Bi-Encoders demonstrate fine-tuning of a minimal fraction of GPT parameters, focusing on STS or retrieval tasks depending on the variant.

  • Non-transformer Context-Aware Model: LASER uses LSTM architecture and is trained on parallel data for bitext mining applications.

Analysis of Task-Specific Performance

  • Classification: ST5 models excel in classification tasks, with ST5-XXL showing particularly high performance.

  • Clustering: MPNet, despite being smaller, competes well with larger models like ST5-XXL. This suggests that fine-tuning on diverse datasets benefits clustering tasks.

  • Pair Classification: GTR-XL and GTR-XXL lead in this area, but models rank differently on STS, underscoring the need for diverse task benchmarking.

  • Reranking: MPNet and MiniLM models show strong performance, possibly due to training dataset overlaps.

  • Retrieval: SGPT-5.8B-msmarco excels in retrieval, while retrieval-specialised models underperform in STS tasks, indicating a division in the field between retrieval-focused and similarity-focused models.

Multilingual Performance

  • Bitext Mining: Dominated by LaBSE, with varying performance across languages.

  • Multilingual Classification and STS: Mixed results, with SGPT-BLOOM-7B1-msmarco performing well in languages it has been pre-trained on.

Key Insights

No Universal Best Model: The benchmark reveals no single model dominates across all tasks, highlighting the need for task-specific model selection.

Trade-off Between Size and Performance: Larger models generally perform better but come with higher computational costs.

Context-Aware vs. Word Embeddings: Context-aware transformer models generally outperform traditional word embeddings but require more computational resources.

Task-Specific Fine-Tuning: The effectiveness of a model can significantly vary based on how it has been fine-tuned and the specific task it is applied to.

Bifurcation of Retrieval and Similarity Tasks: A clear distinction is observed between models optimized for retrieval tasks and those for similarity tasks, indicating different underlying model requirements for these types of tasks.

Multilingual Capabilities: Performance varies significantly across languages, reflecting the challenges in developing truly universal, multilingual embedding models.

The analysis of the Massive Text Embedding Benchmark (MTEB) provides an understanding of the current landscape of embedding tools and their effectiveness across various tasks.

Leveraging these insights, we can explore creative ideas for new embedding tools that address specific challenges or unexplored areas in the field,

Here are some innovative ideas for new embedding tools, followed by a summary of the current best tools for different tasks as indicated by the MTEB analysis:

Creative Ideas for New Embedding Tools

Multimodal Embedding Generator: Develop an embedding tool that can process and integrate multiple types of data (text, audio, visual) to create rich, multimodal embeddings. This would be particularly useful for applications that require understanding content across different media formats, such as social media analysis or multimedia content categorization.

Dynamic Temporal Embeddings: Create embeddings that evolve over time to capture the changing meanings or relevance of words and phrases. This tool could be especially useful in fields like trend analysis, where the significance and context of terms can shift rapidly.

Cross-Cultural Embedding Tool: Develop a tool that focuses on cross-cultural nuances in language, capable of understanding idioms, slang, and culturally specific references. This would be invaluable for global sentiment analysis, marketing, and cultural studies.

Interactive Embedding Visualiser: A tool that not only generates embeddings but also provides an interactive visualisation platform. Users could explore how different words or phrases relate to each other in the embedding space, which would be beneficial for educational purposes and to help researchers develop better embeddings.

Domain-Specific Embedding Optimiser: Given the variability of model performance across tasks, a tool that optimises existing embeddings for specific domains (like legal, medical, or technical fields) would be highly beneficial. This tool could fine-tune general embeddings to make them more effective for specialized applications.

Embedding Personalisation Engine: A tool that creates user-specific embeddings based on their interaction with content. This could be used for personalised recommendation systems or tailored content generation.

Low-Resource Language Embedding Enhancer: Focus on developing embeddings for languages that have limited digital resources available. This tool could use techniques like transfer learning from resource-rich languages to improve NLP capabilities in underrepresented languages.

Current Best Embedding Tools for Different Tasks (Based on MTEB)

Classification: ST5 models (e.g., ST5-XXL) showed the highest average performance in classification tasks.

Clustering: MPNet and MiniLM demonstrated strong performance in clustering tasks, competing well even with larger models.

Pair Classification: GTR-XL and GTR-XXL were noted for their strong performance in pair classification tasks.

Reranking: MPNet and MiniLM again performed strongly in reranking tasks, especially in specific datasets like SciDocsRR.

Retrieval: SGPT-5.8B-msmarco excelled in retrieval tasks, particularly in the BEIR benchmark subset.

STS (Semantic Textual Similarity): For STS tasks, ST5-XXL had the highest performance, indicating its effectiveness in capturing semantic similarities.

Bitext Mining and Multilingual Tasks: LaBSE dominated in bitext mining, while performance in multilingual classification and STS was mixed, with SGPT-BLOOM-7B1-msmarco performing well in certain languages.

LogoMTEB: Massive Text Embedding BenchmarkarXiv.org
Massive Text Embedding Benchmark paper
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