Zhihu question: What is the difference between the Embedding layer of a large model and an independent Embedding model?

Today I saw a question on Zhihu: What is the difference between the embedding layer of a large model and an independent embedding model? I specially compiled it into an article for your reference.

1. Introduction

Imagine if we want a computer to understand the word "apple", we need to convert it into numbers.EmbeddingWhat (embedding representation) does is convert text into a numeric vector that computers can understand.

In the development of AI, we have gone through two important stages: the early independent embedding models (such asWord2Vec), as well as the Embedding layer integrated in the current large model (such asGPT,BERTAlthough both seem to be doing the same thing, the principles and effects behind them are fundamentally different.

The core difference can be summarized in one sentence: the embedding layer of the large model is the internal part that serves the "generation" task, while the independent embedding model is the final product that focuses on "understanding and retrieval". Their goals, training methods and optimization directions are completely different.

Today we will discuss in depth: What is the difference between the embedding layer of a large model and an independent embedding model? Which one is better? What scenarios are they suitable for?

2. Traditional independent embedding model: professional "translator"

2.1 What is an independent embedding model?

independentEmbeddingModels are like specialized "translators" with a clear mission: to translate words into digital vectors so that words with similar meanings are close to each other in the digital space.

Representative models:

• •  Word2Vec : Learning word vectors by predicting context
• •  GloVe : Based on global word frequency statistics
• •  FastText : considers the character information of words

2.2 How are they trained?

byWord2VecFor example, the training process is very similar to how we learn a language:

Input sentence: "I like to eat apples"
Training goal: Seeing "I like to eat", be able to predict "apple"
Or: seeing "apple" can predict "I like to eat"

By practicing this a lot, the model learns to:

• • "Apple" and "Banana" should be similar (both are fruits)
• • There should be a big gap between "Apple" and "car"

Code example:

# Modern standalone Embedding model usage examples
from  sentence_transformers  import  SentenceTransformer
import  numpy  as  np
from  sklearn.metrics.pairwise  import  cosine_similarity

# Load the pre-trained model
model = SentenceTransformer( 'paraphrase-multilingual-MiniLM-L12-v2' )

# Encode sentences
sentences = [ "This movie is great" ,  "This movie is wonderful" ,  "The weather is nice today" ]
embeddings = model.encode(sentences)

# Calculate similarity
similarity_matrix = cosine_similarity(embeddings)
print ( f "Similarity between sentence 1 and sentence 2:  {similarity_matrix[ 0 ][ 1 ]: .3 f} " )

2.3 Comparative Learning and Training Methods for Independent Embedding

Modern independenceEmbeddingModels are usually trained using a method called Contrastive Learning:

Training Data:

• • A large number of text pairs, including positive examples (semantically similar sentence pairs, such as "How to apply for a passport in Beijing?" and "What is the process of applying for a passport in Beijing?")
• • Negative examples (semantically unrelated sentence pairs)

Loss function objective:

• • In vector space, minimize the distance between positive pairs of vectors
• • Maximize Distance: vector distance of negative pairs

Training results: This training method "forces" the model to learn the core semantics of the sentence (
Semantic Meaning), rather than just the surface grammar or word order. Therefore, the vector it generates performs extremely well in measuring whether the sentences are "similar in meaning" and is specially tailored for tasks such as semantic search, clustering, and RAG (retrieval-augmented generation).

Features summary:

• •  ✅Specialized optimization : focus only on learning the semantic relationship of words
• •  ✅Efficient training : The model is relatively simple and the training speed is fast
• •  ✅Versatile : train once, use everywhere
• • ✅  Retrieval optimization : specially designed for semantic comparison and retrieval tasks
• •  ❌Static representation : Each word has only one fixed vector ("bank" cannot distinguish between a financial institution and a river bank)

3. Technical mechanism of large model embedding

3.1 Working Mechanism

In large models such as GPT and BERT, the Embedding layer is no longer an independent layer, but the first layer of the entire model. Modern large language models use the **End-to-End Joint Training** method, and all parameters (including the Embedding matrix) serve the same ultimate goal - improving the accuracy of language modeling.

Core Features:

• •  Integrated design : Deep integration of the Embedding layer and the Transformer layer
• •  Joint optimization : All parameters are updated synchronously to ensure global optimization
• •  Context-aware : The representation of each token is influenced by the global context
• •  Dynamic adjustment : Generate different semantic representations based on different contexts

3.2 Training process and technical details

3.2.1 End-to-end training process

1. Initialization phase :

• •  Parameter initialization : Randomly initialize the Embedding matrix
• •  Matrix structure : parameter matrix of dimension [vocab_size, hidden_dim]
• •  Initialization strategy : Use Xavier or He initialization to ensure stable gradient propagation
• •  Parameter scale : Taking GPT-3 as an example, the vocabulary is 50K, the hidden dimension is 12288, and the number of parameters in the Embedding layer is about 600 million

2. Forward propagation stage :

# Pseudocode example
input_ids = tokenizer( "Hello world" )   # [101, 7592, 2088, 102]
token_embeddings = embedding_matrix[input_ids]   # table lookup operation
position_embeddings = get_position_encoding(seq_length)
final_embeddings = token_embeddings + position_embeddings

3. Loss calculation and parameter update :

• •  Prediction task : given the first n tokens, predict the n+1th token
• •  Loss function : cross entropy loss L = -log P(token_true | context)
• •  Gradient propagation : Output layer → Transformer layer → Embedding layer
• •  Joint optimization : All parameters are updated synchronously to ensure global optimization

3.2.2 Detailed explanation of position encoding mechanism

Why is it necessary?  Transformer’s self-attention mechanism is permutation invariant and cannot distinguish word order

Comparison of actual effects:

Scenario: "Apple launches new product"

❌ No positional encoding:
"Apple releases new product" ≈ "product new release company Apple"
The model cannot understand word order and the semantics are confusing

✅ With position coding:
"Apple Inc." → Identified as a technology business entity
"Release new products" → understood as a commercial activity
Full semantics: product launch events for technology companies

3.3 Core Features

3.3.1 Dynamic Semantic Encoding

Large ModelEmbeddingThe core advantage of is its dynamic nature. The same word will have different vector representations in different contexts, which enables the model to accurately capture the polysemy of words and context-related semantic changes.

Dynamic performance:

• •  Before training : random vectors, no semantic information
• •  During training : gradually learning the semantic representation and positional relationship of words
• •  After training : Each vector carries rich contextual semantics

Example: Context-sensitive semantic representation :

• • "Apple released a new product" → vectors tend to be more technological and business-oriented
• • "Apple is sweet and delicious" → The vector is biased towards food and taste semantics

3.3.2 Context understanding ability

The large model can dynamically adjust the semantic representation of each word according to the global context, which is different from independentEmbeddingcore advantage.

Examples of contextual understanding benefits:

Consider the sentence "Bank interest rates have risen":

• •  Independent Embedding : "Bank" is always mapped to a fixed vector
• •  Large model embedding : The vector representation of "bank" in the financial context will be closer to concepts such as "interest rate" and "finance"

3.3.3 Influence of training objectives

The training objectives of large models directly affect the representation capabilities of their Embedding layers:

Optimize direction differences:

• •  Independent models : focus on static relationships between words (such as Word2Vec’s Skip-gram objective)
• •  Large model : optimizes overall language understanding capabilities, and embedding as a by-product obtains richer semantic representation

4. Core Difference Comparison and Performance Evaluation

4.1 Technical Comparative Analysis

4.1.1 Core Technology Differences

4.1.2 Comparison of polysemous word processing capabilities

Example analysis: For the word "open":

• • "Open file" → Computer operation semantics
• • "Open your heart" → emotional expression semantics
• • "Opening the market" → Business development semantics

Comparison of processing methods:

• •  Independent Embedding : All "open" are mapped to the same fixed vector
• •  Large model Embedding : Generate different semantic vectors for the same word based on the context

4.1.3 Comparison of training paradigms

Independent training features:

• • ?  Targeted : Optimize word similarity
• •  Data efficient : no need for ultra-large-scale data
• •  ⚡Fast training : simple model, fast convergence
• •  Reusable : train once, use in many places
• • ?  Search optimization : specially designed for semantic search

Joint training features:

• • ?  End-to-end : all parameters are optimized together
• • ?  The goal is complex : learning representations in language modeling
• •  Data intensive : requires massive amounts of training data
• • ?  Task-oriented : optimized for specific tasks
• •  Context-aware : dynamically understand word meanings

4.2 Performance Test Results

4.2.1 Text Similarity Task

Note: The numbers are for reference only, actual performance depends on the model, data and hardware configuration

4.2.2 Word Analogy Task

Task : "King - Man + Woman =?" (The answer should be "Queen")

4.2.3 Semantic Retrieval Task

Task : Find content in a large number of documents that is semantically relevant to the query

Performance evaluation method:

Intrinsic Evaluation :

• • Word Similarity Task
• • Word Analogy
• • Clustering Quality

Extrinsic Evaluation :

• • Downstream Task Performance
• • Retrieval Evaluation
• • Classification Accuracy

4.3 Application Scenario Analysis

Based on the above performance test results, different models show obvious advantages in their respective areas of expertise. Detailed model selection guidelines and practical decision-making processes will be introduced in detail in Chapter 6.

5. In-depth thinking on "generalized embedding model"

5.1 LLM is essentially a generalized Embedding model

In a sense, a completeLLMcan be seen as an extremely powerful and complex "generalEmbeddingmodel" or "feature extractor".

Traditional Embedding Model:

• • Input a sentence and output a vector of fixed dimension (Embedding)
• • This vector represents the semantic compression of the entire sentence
• • Example: "The cat sat on the mat." → [0.1, 0.5, -0.2, ...] (768 dimensions)

Large Language Model (LLM):

• • Input a sentence (or longer text), after being processed by the Embedding layer and N Transformer Blocks
• • The output of the last hidden layer (Final Hidden State) can be seen as an extremely rich "contextualized embedding" of the sentence in a very high dimension
• • For example: "The cat sat on the mat." → [, , ..., <vector_for_.>] (each token has a high-dimensional vector, such as 4096 dimensions)

5.2 Redefine Embedding

Traditional concept: Embedding = word vector representation New understanding: Embedding = any representation learning that converts discrete symbols into continuous vector space

From this perspective:

Traditional: word → vector
Large model: sentence/paragraph → vector (taking into account more complex context and semantic relationships)

5.3 Hierarchical Semantic Extraction

Every layer in a large model is doing some form of "embedding":

Input layer: word → basic semantic vector
Layer 1: Basic semantics → local grammatical relation vector
Layer 2: Local relations → Syntactic structure vector
...
Layer N: Complex semantics → High-level abstract vectors

It's like:

• •  Level 1 : Understanding vocabulary meaning
• •  Level 2 : Understanding phrase collocation
• •  Level 3 : Understanding sentence structure
• •  Higher level : understanding paragraph logic, document theme

5.4 The deeper meaning of "contextualized semantic representation"

In the generative model, the final hidden state vector contains all the model's understanding of the input text - lexical semantics, syntactic structure, contextual relationships, and even world knowledge - and encodes it all for the sole purpose of generating the next step. This vector contains all the information needed to predict the "next word" and can be considered a perfect embodiment of the "future potential semantics of the entire sentence."

Current status: "Today's weather is very good"
The internal representation of the model includes:
- The semantics of the current information
- Probability distribution of possible continuations (good, hot, cold, sunny, etc.)
- Anticipation of the possible semantic direction of the entire sentence

5.5 The essential difference between the two

butLLM"Broad senseEmbedding"With independenceEmbeddingThe difference between the models is:

• •  Purpose : LLM's "broad senseEmbedding" is its internal "mental state" used for generation; while the independent modelEmbeddingis the final output for retrieval and comparison
• •  form :LLMThe output is eachTokenCorresponds to a sequence of vectors, while independent models usually output a single vector representing the entire sentence/paragraph (achieved through operations such as pooling)
• •  Efficiency : Directly use the last hidden layer of LLM as a generalEmbedding, not only is the dimension too high and the computational cost huge, but the effect may not be as good as that of a specially optimized independent model

6. Application scenarios, selection strategies and hybrid solutions

6.1 Model Selection Guidelines

6.2 Practical Application of Hybrid Strategies

In practical applications, we can adopt a mixed strategy.RAG(Retrieval-Augmented Generation) system is closely related:

Relationship with RAG:

• •  Architectural similarity : The two-stage process of the hybrid strategy is exactlyRAGThe core idea of ​​the system retrieval part
• •  Technology stack overlap :RAGThe retrieval stage usually adopts "lightweightEmbedding"Coarse screening + re-sorting and selection" method
• •  Consistent application scenarios : both are widely used in knowledge question answering, document retrieval and other scenarios

Key Differences:

• •  Scope of application : Hybrid strategy focuses onEmbeddingIndicates optimization,RAGCovering the complete process of "retrieval + generation"
• •  Ultimate goal : Hybrid strategy pursues better semantic representation,RAGPursuing high-quality text generation
• •  Technical focus : Hybrid strategy focuses on representation learning,RAGIt is also necessary to process the fusion of retrieval results and generated models

Specific implementation:

Stage 1: Rough screening using independent embeddings
         Quickly filter out obviously irrelevant content
         (Corresponding to the vector retrieval stage in RAG)

Phase 2: Using large model embedding for precise understanding
         Perform deep semantic analysis on candidate content
         (Corresponding to the reordering or exact matching stage in RAG)

6.3 Practical Decision-Making Process

Model selection decision tree

1. 1.  Resource Constraint Assessment

• • Latency requirement: <100ms → Independent Embedding
• • Memory limit: <500MB → Independent Embedding
• • Computing resources: GPU is not available → Independent Embedding

• 2.  Task complexity assessment
• • Contextual understanding is required → Large model embedding
• • Polysemy Sensitivity → Large Model Embedding
• • Simple matching task → Independent Embedding
• 3.  Performance requirements assessment
• • Retrieval accuracy first → Dedicated Embedding model
• • Generality first → Large model embedding

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7. Summary and Outlook

7.1 Summary of core ideas

1. 1.  Essential differences :
• • Independent Embedding focuses on lexical semantic relationships and retrieval optimization
• • Large model embedding focuses on contextual understanding and generation tasks
• 2.  Application selection :
• • Semantic retrieval tasks: standalone models are usually better and more efficient
• • Contextual understanding tasks: Large models are significantly better
• • Resource-constrained environments: Standalone models preferred
• • Complex NLP tasks: prefer large models
• 3.  Development trend :
• •  Efficiency optimization : model compression, lightweight design
• •  Multimodal fusion : unified representation of text + image + audio
• •  Chinese optimization : BGE, E5 and other specially optimized Chinese models
• •  Command control : control Embedding behavior through natural language commands

7.2 Practical suggestions

Selection Decision:

• • Pursuit of efficiency → Independent Embedding
• • Pursuing effects → Large model embedding
• • Specialized Search → Independent Model
• • Common understanding → Big model

Final Thoughts:

Independent embedding and large model embedding are complementary rather than competitive. Understanding their differences and connections can help us make more informed choices in practical applications, avoiding both over-design and under-design.

The evolution from Word2Vec to GPT is not only a technological advancement, but also a deepening of our understanding of language. Each technological breakthrough brings us closer to the goal of "making machines truly understand human language."

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