Supercharging LLMs With Knowledge Graphs for Smarter, Fairer AI

Hey, folks. I’m an AI geek who’s spent years wrestling with large language models (LLMs) like GPT-4. They’re incredible — chatting, coding, reasoning like champs — but they’ve got a flaw: they’re trained on the wild web, soaking up biases like gender stereotypes or racial skews. 

Picture an LLM skipping a top-notch female data scientist because it’s hung up on “tech = male.” That’s a real danger in hiring or healthcare apps, and it’s why I’ve poured my energy into Knowledge Graph-Augmented Training (KGAT).

In this tutorial, I’ll share my approach. Straight from my work, like Detecting and Mitigating Bias in LLMs through Knowledge Graph-Augmented Training (Zenodo) with code and steps to try it yourself!

The Bias Mess: Why I Dug In

LLMs feast on internet chaos — tweets, blogs, the works — and inherit our messy biases. Feed one resumes, and it might favor “Mike” over “Maya” for a coding gig, echoing old patterns. My experiments with Bias in Bios showed this isn’t just talk — gender and racial skews pop up fast. 

Old fixes like data tweaks or fairness rules? They’re quick patches that don’t tackle the root or keep the model’s spark alive. That’s why I turned to knowledge graphs (KGs) — my game-changer.

KGAT: My Fix for Better AI

Imagine a knowledge graph as a fact-web — nodes like “engineer” or “woman” linked by edges like “works as.” My KGAT method, detailed in my enterprise intelligence paper, pairs this structured map with LLMs to cut bias and boost smarts. Here’s my playbook:

1. Pick an LLM: I start with a beast like GPT-4.
2. Add a KG: I hook it to a factual graph (Wikidata or custom) full of real connections.
3. Train smart: Fine-tune it to cross-check text guesses with KG facts.

This isn’t just about ethics — my enterprise pilots hit a 20% productivity spike! It’s in my Detecting and Mitigating Bias in LLMs talk at AIII 2025 (schedule). KGAT’s a business turbocharger, too.

Hands-On: Build It With Me

Let’s code up my KGAT pipeline. Here’s how I roll:

1. Prep the Data

I use datasets like these to test bias and brains:

• Bias in Bios: Resumes with job/gender tags (source).
• FairFace: Faces with race/gender labels (source).
• COMPAS: Recidivism data for fairness (source).

Clean lowercase text, ditch noise, and link entities (e.g., “data scientist”) to Wikidata. I keep it basic with simple entity matching for starters.

2. Wire Up the KG

I lean on graph neural networks (GNNs) to turn KGs into vectors that LLMs can digest. My setup:

import torch

from torch_geometric.nn import GCNConv

from transformers import GPT2Tokenizer, GPT2Model

# Load LLM (GPT-2 for this demo)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')

# My GNN layer (KG—swap in yours)
gcn = GCNConv(in_channels=128, out_channels=768)  # Match LLM dims
kg_nodes = torch.rand(10, 128)  # 10 nodes, 128-dim features
kg_edges = torch.tensor([[0, 1], [1, 2], [2, 0]])  # Simple edges

kg_emb = gcn(kg_nodes, kg_edges)  # KG vectors ready

3. Blend and Train

I merge LLM and KG embeddings with my formula: E_integrated = E_LLM ⊕ E_KG (just glue ‘em together).

Training kickoff:

# text embeddings (use your tokenized data)
text_emb = torch.rand(32, 768)  # Batch of 32, 768-dim
integrated_emb = torch.cat([text_emb, kg_emb[:32]], dim=1)  # Match sizes

# Fine-tune (super simplified)
outputs = model(inputs_embeds=integrated_emb)
loss = outputs.loss  # Add a real loss later
loss.backward()  # Optimize with Adam soon

print("KGAT’s rolling!")

For real runs, I use Adam (learning rate 3e-5, batch size 32, 10 epochs) — my go-to from the bias work.

4. Hunt Down Bias

I track bias with metrics I swear by:

• Demographic parity: Equal positives across groups.
• Equal opportunity: Fair true-positive rates.

Quick test:

from sklearn.metrics import confusion_matrix

# Dummy preds vs. truth
y_true = [0, 1, 0, 1]
y_pred = [0, 1, 1, 0]

tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
equal_opp = tp / (tp + fn)

print(f"Equal Opportunity: {equal_opp:.2f}")

My results? Bias in Bios parity up 15%, COMPAS fairness up 10% — huge for trust in real apps.

Why This Fires Me Up (and Should You)

KGAT’s my passion because:

• Fairness counts: Biased AI can tank your app or harm users — I’m here to stop that.
• Scales big: My framework flexes with Wikidata or your own KG — enterprise-ready.
• Smarter AI: That 20% productivity lift? It’s KGs making LLMs brilliant, not just nice.

Picture a hiring bot without KGAT; it skips “Priya” for “Pete.” With my method, it sees “data scientist” isn’t gendered and picks the best.

Watch Out: My Hard-Earned Tips

KGAT’s not perfect — I’ve hit snags:

• KG quality: A weak graph (e.g., outdated roles) can flop. I vet mine hard.
• Compute load: GNNs and LLMs need power — I lean on GPUs or the cloud.
• Big data: Millions of records? I chunk it or go parallel.

Try It Out: My Challenge to You

Start small with my approach:

1. Grab Bias in Bios and a Wikidata slice.
2. Use torch-geometric for GNNs and transformers for GPT-2 (or GPT-4 if you can).
3. Tweak my code. Add real embeddings and a loss like cross-entropy.

My pilots and bias talks show these scales — your next project could rock with it.

My Take: Let’s Build Better AI

KGAT’s my ticket to LLMs that don’t just dazzle but deliver — fair, smart, and ready to roll. It’s not just research; it’s hands-on and proven in my work. Fire up that code, test a dataset, and share your wins below. I’m stoked to see what you do with it!

Dig deeper? Check my presentation on Zenodo or join me at DZone!