created with Gemini
Are Encoder-Decoder Models Less Prone to Hallucination? A data-driven comparison of T5/Flan and GPT model architectures, grounded in recent AI research.
The Critical Challenge of AI Hallucination Hallucination, where Large Language Models (LLMs) generate factually incorrect or nonsensical information, is a primary barrier to their reliable deployment. Understanding which architectural designs are inherently more robust is crucial. This analysis explores the structural differences between encoder-decoder (e.g., T5) and decoder-only (e.g., GPT) models and how these differences impact their tendency to hallucinate, referencing key academic findings.
Architectural Blueprints: How They Differ Encoder-Decoder Models (e.g., T5, Flan-T5) These models process the input text with an 'encoder' to create a comprehensive, context-rich understanding. A 'decoder' then uses this fixed understanding as a strong guide to generate the output. This two-step process inherently anchors the output to the source material.
Input Text → Encoder (Builds Context) → Decoder (Generates Output) Decoder-Only Models (e.g., GPT Series) These models are autoregressive, meaning they predict the next word based on the prompt and all the words they have generated so far. Without a separate, holistic encoding of the input, the model's focus can drift during long generations, increasing the risk of deviating from source facts.
[Input + Output] ↻ Decoder (Predicts Next Word) Comparative Hallucination Tendency Research suggests a structural advantage for encoder-decoder models in fact-grounded tasks. A study by Guerreiro et al. (2023) found them less prone to hallucination on knowledge-intensive tasks, attributing this to the encoder's grounding mechanism.
This chart is a conceptual representation based on findings that encoder-decoder models show lower hallucination rates in source-grounded tasks compared to decoder-only models, especially before extensive instruction tuning.
Key Factors Influencing Hallucination
-
Source Grounding The encoder's representation provides a strong, persistent "ground truth" for the decoder. This makes it harder for the model to invent facts, as it is constantly guided by the source content. Decoder-only models rely on attention mechanisms that can "forget" or de-prioritize initial context over longer outputs.
-
Training Objective T5's "denoising" objective (filling in missing text) trains it to be highly faithful to surrounding context. GPT's "next-token prediction" is less constrained, optimizing for fluency and coherence, which can sometimes come at the cost of factual accuracy if the training data contains plausible falsehoods.
-
Instruction Tuning (The Great Equalizer) Fine-tuning on diverse sets of instructions (as in Flan-T5 and InstructGPT) dramatically reduces hallucinations for both architectures. The "Scaling Instruction-Finetuned Language Models" paper (Chung et al., 2022) showed that this technique teaches models to be more helpful, honest, and harmless, directly improving factuality.
Evidence from Research The TruthfulQA benchmark was designed to measure a model's tendency to generate common falsehoods. While newer, instruction-tuned models from both families perform better, the results highlight the profound impact of scaling and fine-tuning.
Illustrative data showing general performance trends on factuality benchmarks. Absolute scores vary, but the trend of instruction-tuned models outperforming base models is consistent across research.
Conclusion: Structure Matters, But Tuning is King Encoder-decoder models like T5 and Flan have a structural advantage that makes them inherently less prone to hallucination in tasks requiring faithfulness to a provided source. The encoder acts as a strong factual anchor, constraining the decoder's output.
However, this is not the whole story. The single most important factor in mitigating hallucination is large-scale instruction tuning. As research from papers like "A Survey on Hallucination in Natural Language Generation" (Ji et al., 2023) confirms, fine-tuning improves factuality across all architectures.
Ultimately, while T5's architecture provides a better "safety rail" against drifting from facts, a well-tuned decoder-only model like modern versions of GPT can also achieve high levels of factuality. The choice depends on the specific application's need for strict source grounding versus creative generation.