Tech Matchups: GPT-3 vs LaMDA
Overview
GPT-3 is a large language model excelling in text generation and versatility.
LaMDA is designed for natural, context-aware conversational AI.
Both enhance dialogue: GPT-3 for breadth, LaMDA for coherence.
Fun Fact: GPT-3 has 175 billion parameters!
Section 1 - Architectural Differences
GPT-3 Core Architecture:
# GPT-3's Decoder-Only Transformer
class GPT3Block(nn.Module):
def __init__(self, hidden_size=12288, num_heads=96):
self.attn = MultiHeadAttention(hidden_size, num_heads)
self.mlp = nn.Sequential(
nn.Linear(hidden_size, 4*hidden_size),
nn.GELU(),
nn.Linear(4*hidden_size, hidden_size)
)
self.ln_1 = LayerNorm(hidden_size)
self.ln_2 = LayerNorm(hidden_size)
def forward(self, x):
# 96 attention heads with rotary positional embeddings
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
LaMDA's Dialog-Specific Modifications:
# LaMDA's Safety Layers
class LaMDA(nn.Module):
def __init__(self):
self.base_model = TransformerDecoder(hidden_size=8192)
self.sensitivity_classifier = nn.Linear(8192, 3) # [safe, sensitive, unsafe]
self.grounding_verifier = ExternalKnowledgeChecker()
def generate(self, prompt):
logits = self.base_model(prompt)
# Safety check before decoding
sensitivity = torch.argmax(self.sensitivity_classifier(logits[:,-1]))
if sensitivity == 2: # Unsafe
return "[REDACTED RESPONSE]"
# Knowledge grounding for factual claims
if contains_factual_claim(logits):
grounded = self.grounding_verifier.check(logits)
return grounded
return logits
- Positional Encoding: GPT-3 uses learned positional embeddings, LaMDA employs relative position biases
- Attention Patterns: LaMDA limits lookahead in dialog turns to prevent topic drift
- Memory: LaMDA maintains explicit conversation state beyond context window
Section 2 - Training Data Composition
GPT-3 Training Corpus:
| Source | Percentage | Token Count |
|---|---|---|
| Common Crawl | 60% | 180B |
| WebText2 | 22% | 66B |
| Books | 8% | 24B |
| Wikipedia | 3% | 9B |
LaMDA's Dialog-Specific Data:
| Dialog Type | Percentage | Quality Filters |
|---|---|---|
| Public Forum Dialogs | 42% | Engagement > 2.5/5 |
| Annotated Task Dialogs | 31% | Complete goal paths |
| Human-AI Conversations | 27% | Rated >4/5 helpfulness |
Data Quality: LaMDA's training dialogs underwent 3x more filtering than GPT-3's general web text.
Section 3 - Conversational Performance
Google's Internal Dialog Evaluation (100K samples):
| Metric | LaMDA | GPT-3 | Human Baseline |
|---|---|---|---|
| Sensibleness | 86% | 71% | 89% |
| Specificity | 82% | 64% | 84% |
| Factual Grounding | 78% | 62% | 81% |
| Turn Continuity | 91% | 73% | 93% |
Latency Comparison (A100 GPU):
- GPT-3: 350ms/token (175B params, no safety checks)
- LaMDA: 420ms/token (137B params + 80ms safety overhead)
Section 4 - Safety & Alignment
LaMDA's Three-Layer Safety:
# LaMDA's safety pipeline
def safe_generate(prompt):
# 1. Input filtering
if toxicity_classifier(prompt) > 0.8:
return "[INPUT REJECTED]"
# 2. Real-time monitoring
response = base_model.generate(prompt)
# 3. Output verification
safety_scores = {
'toxicity': toxicity_model(response),
'factuality': fact_checker(response),
'sensitivity': sensitivity_detector(response)
}
if any(score > threshold for score in safety_scores.values()):
return "[REDACTED]"
return response
GPT-3's Post-Hoc Filtering:
- Content moderation API applied after generation
- Few-shot learning for desired behaviors
- No built-in fact verification
Reduction in Harmful Outputs: LaMDA's safety layers reduce toxic outputs by 5x compared to GPT-3's post-generation filtering.
Section 5 - Deployment Scenarios
When to Choose GPT-3:
- General text generation (articles, code, etc.)
- Few-shot learning applications
- When maximum creativity is prioritized over safety
When to Choose LaMDA:
- Multi-turn dialog systems
- Applications requiring factual accuracy
- High-stakes environments needing built-in safety
Cost Comparison:
| Model | Cost per 1K tokens | Max Context |
|---|---|---|
| GPT-3 (Davinci) | $0.02 | 4K tokens |
| LaMDA (via Vertex AI) | $0.03 | 8K tokens |
Section 6 - Evolving Architectures
GPT-3's Evolution:
- GPT-4's mixture of experts (8x220B parameters)
- Improved instruction following via RLHF
LaMDA's Development:
- LaMDA 2's improved knowledge grounding
- Integration with Google Search for real-time facts
Emerging Trend: Both architectures are converging toward hybrid approaches combining GPT's generality with LaMDA's safety features.