Detecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing

Architecture & Actionable Implementation

To detect hallucinations in LLMs, we analyze the topological structure of attention graphs using Forman-Ricci curvature to identify information bottlenecks. This method captures both semi-local and global information-flow characteristics associated with hallucinated responses.

import torch
import torch.nn as nn

class AttentionGraphAnalyzer(nn.Module):
    def __init__(self, num_heads, head_dim):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = head_dim
        
    def forward(self, attention_scores):
        # attention_scores shape: (batch_size, num_heads, seq_len, seq_len)
        batch_size, _, seq_len, _ = attention_scores.shape
        
        # Calculate Forman-Ricci curvature
        forman_ricci_curvature = self.calculate_forman_ricci_curvature(attention_scores)
        
        # Identify information bottlenecks
        bottlenecks = self.identify_bottlenecks(forman_ricci_curvature)
        
        return bottlenecks
    
    def calculate_forman_ricci_curvature(self, attention_scores):
        # Implement Forman-Ricci curvature calculation
        pass
    
    def identify_bottlenecks(self, forman_ricci_curvature):
        # Identify information bottlenecks based on curvature
        pass

# Example usage
analyzer = AttentionGraphAnalyzer(num_heads=8, head_dim=64)
bottlenecks = analyzer(attention_scores)

Key Components

• Attention Graph Analysis: Analyze the topological structure of attention graphs to identify information bottlenecks.
• Forman-Ricci Curvature: Calculate the curvature of attention graphs to identify structural patterns indicating information bottlenecks.
• Bottleneck Identification: Identify information bottlenecks based on the curvature of attention graphs.

Hard Production Trade-Offs (The Cost of Scale)

Latency vs. Throughput

Our method introduces minimal latency overhead while improving hallucination detection performance. The single-pass approach ensures consistent throughput without significant queueing delays.

Unit Economics & TCO

The method is computationally efficient, reducing the need for multiple response generations, which lowers the total cost of ownership (TCO) for hallucination detection.

Failure Domains

Potential failure domains include misidentification of non-hallucinated responses as hallucinated and vice versa. Continuous monitoring and model updates are necessary to mitigate these risks.

Benchmark Radar Capability Data