How does GPT differ from BERT, and what are generative pre-trained transformers good at?

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AcadiFi · Accepted Answer

GPT (Generative Pre-trained Transformer, Radford et al. 2018) is a Transformer decoder trained autoregressively: predict the next token given all previous tokens.

Key architectural differences vs BERT:
| Aspect | BERT | GPT |
|---|---|---|
| Architecture | Encoder only | Decoder only |
| Attention | Bidirectional | Causal (mask future tokens) |
| Pre-training | Masked LM | Next-token prediction |
| Typical use | Classification, embedding | Generation, completion |

GPT is trained to maximize P(token_t | token_1, ..., token_{t-1}) on massive corpora. At inference, it samples one token at a time.

Scale matters: GPT-1 had 117M parameters, GPT-2 1.5B, GPT-3 175B, GPT-4 estimated > 1T. Emergent abilities appear at scale - few-shot learning, chain-of-thought reasoning, tool use.

Finance use cases:
- **Research summarization**: condense 80-page 10-Ks into actionable bullet points.
- **Code generation**: write Python backtesting scripts from natural language prompts.
- **Synthetic data augmentation**: generate plausible financial text for training smaller classifiers.
- **Compliance screening**: check marketing materials against SEC Rule 206(4)-1.
- **Conversational research assistants**: Morgan Stanley's AI @ Morgan Stanley uses GPT-4.

Limitations and CFA-relevant risks:
- **Hallucination**: GPT generates fluent but factually wrong content. Never trust financial figures without source verification.
- **Training data cutoff**: GPT doesn't know recent events unless retrieved.
- **Prompt injection**: malicious content in retrieved documents can subvert agent behavior.
- **IP and regulatory concerns**: sending client data to third-party APIs violates confidentiality agreements.

Mitigations: retrieval-augmented generation (RAG) grounds outputs in verified sources; structured output (JSON schemas) constrains responses; guardrail systems (Llama Guard, etc.) filter risky outputs.

For CFA: GPT changes the research workflow but doesn't change fiduciary duties - you still own the analysis.

How does GPT differ from BERT, and what are generative pre-trained transformers good at?

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