Semantic Attention Mechanisms

Enhancing LLMs with Ontology-Aware Attention

This guide explores how to enhance attention mechanisms in LLMs with semantic knowledge from ontologies, focusing on improving model interpretability and performance in specialized domains like plant disease diagnosis.

Why Semantic Attention?

The Challenge with Standard Attention

Standard attention mechanisms: - Treat all tokens equally without domain knowledge - May focus on irrelevant context - Lack structured reasoning capabilities

The Solution: Ontology-Guided Attention

graph LR
    A[Input Tokens] --> B[Standard Attention]
    A --> C[Ontology Knowledge]
    B --> D[Attention Weights]
    C --> D
    D --> E[Context-Aware Output]

Implementation

1. Ontology-Aware Attention Layer

import torch
import torch.nn as nn
from typing import Optional, Tuple

class OntologyAttention(nn.Module):
    def __init__(self, embed_dim: int, num_heads: int, ontology_dim: int = 64):
        super().__init__()
        self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        self.ontology_proj = nn.Linear(embed_dim, ontology_dim)
        self.ontology_weights = nn.Parameter(torch.randn(1, num_heads, 1, ontology_dim))
        
    def forward(self, 
               query: torch.Tensor,
               key: torch.Tensor,
               value: torch.Tensor,
               ontology_embeddings: Optional[torch.Tensor] = None,
               key_padding_mask: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
        
        # Standard multi-head attention
        attn_output, attn_weights = self.multihead_attn(
            query, key, value, 
            key_padding_mask=key_padding_mask,
            need_weights=True
        )
        
        if ontology_embeddings is not None:
            # Project ontology knowledge
            proj_ontology = self.ontology_proj(ontology_embeddings)
            
            # Calculate ontology-aware attention adjustment
            ontology_scores = torch.einsum('bhsd,bd->bhs', 
                                        self.ontology_weights.expand(query.size(1), -1, -1, -1),
                                        proj_ontology)
            
            # Combine with standard attention
            attn_weights = attn_weights + ontology_scores.softmax(dim=-1)
            
            # Renormalize
            attn_weights = attn_weights / attn_weights.sum(dim=-1, keepdim=True)
            
            # Apply final attention
            attn_output = torch.bmm(attn_weights, value.transpose(0, 1)).transpose(0, 1)
        
        return attn_output, attn_weights

2. Integration with Transformers

class OntologyAwareTransformerLayer(nn.TransformerEncoderLayer):
    def __init__(self, d_model: int, nhead: int, dim_feedforward: int = 2048, 
                 dropout: float = 0.1, activation: str = "relu",
                 ontology_dim: int = 64):
        super().__init__(d_model, nhead, dim_feedforward, dropout, activation)
        
        # Replace standard self-attention with ontology-aware attention
        self.self_attn = OntologyAttention(d_model, nhead, ontology_dim)
        
    def forward(self, src: torch.Tensor, ontology_emb: Optional[torch.Tensor] = None,
                src_mask: Optional[torch.Tensor] = None,
                src_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        
        # Self-attention with ontology guidance
        src2, attn_weights = self.self_attn(
            src, src, src,
            ontology_embeddings=ontology_emb,
            key_padding_mask=src_key_padding_mask
        )
        
        # Rest of the transformer layer
        src = src + self.dropout1(src2)
        src2 = self.norm2(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
        src = src + self.dropout2(src2)
        
        return src, attn_weights

Case Study: Plant Disease Diagnosis

1. Attention Visualization

def visualize_attention(text: str, attention_weights: torch.Tensor, ontology_terms: List[str]):
    """Visualize attention weights with ontology term highlighting."""
    tokens = tokenizer.tokenize(text)
    
    plt.figure(figsize=(12, 6))
    plt.imshow(attention_weights, cmap='viridis')
    
    # Highlight ontology terms
    for i, token in enumerate(tokens):
        if token.lower() in ontology_terms:
            plt.axvline(i, color='red', alpha=0.1)
            plt.text(i, -0.5, token, rotation=45, ha='right')
    
    plt.colorbar()
    plt.xlabel('Tokens')
    plt.ylabel('Attention Heads')
    plt.title('Ontology-Aware Attention Weights')
    plt.tight_layout()
    plt.show()

Best Practices

1. Ontology Embedding

  • Use pre-trained ontology embeddings
  • Fine-tune embeddings on your specific task
  • Consider hierarchical relationships

2. Training Strategy

  • Start with warm-up phase without ontology guidance
  • Gradually increase ontology influence
  • Monitor attention patterns during training

3. Evaluation

  • Measure attention alignment with domain knowledge
  • Compare with baseline attention mechanisms
  • Conduct human evaluation of attention patterns

Next Steps

  1. Implement the OntologyAttention layer in your model
  2. Prepare ontology embeddings for your domain
  3. Fine-tune with domain-specific data
  4. Evaluate attention patterns and model performance

References