GPT-5's reduction in hallucination rates is attributed to both its training data curation and advanced training methodologies. OpenAI has openly reported that GPT-5 responses are up to 45% less likely to contain factual errors compared to GPT-4o, and with its advanced "reasoning" mode, factual errors drop by about 80% relative to the prior o3 model. The suppression of hallucinations in GPT-5 is not the result of a single dataset, but a sophisticated process of dataset assembling, filtering, continuous post-training with human feedback, and integration of external factual checking resources.
Data Quality and Curation Strategy
OpenAI's first pillar against hallucinations in GPT-5 is the use of expanded, high-quality, and curated datasets. This means:
- Source data is more likely to be verified and reputable.
- Explicit efforts are made to remove or minimize known unreliable, biased, or malicious content during pre-training and during data refresh cycles.
- User-contributed data is filtered, anonymized, and scrutinized for facticity before inclusion in supervised fine-tuning or reward modeling.
To further reduce hallucination risk, OpenAI has deployed extensive data cleaning processes to identify and exclude noisy, contradictory, or synthetic content that could induce errors in the model's outputs.
Post-Training and Reinforcement From Human Feedback (RLHF)
Human feedback is central in GPT-5's architecture. The model undergoes intensive rounds of reinforcement learning from human feedback (RLHF), in which human raters:
- Judge outputs for factual correctness, coherence, and alignment with user intent.
- Provide pairwise preferences on model generations, rewarding accuracy and informativeness while penalizing hallucinations.
- These signals form the basis for reward models that further optimize GPT-5 to prefer factually correct completions.
Additionally, RLHF is augmented by automated factuality graders validated against human judgment to scale the detection of hallucinations. These graders serve both as a quantitative yardstick in evaluations and as a component of continual training, enabling large-scale, rapid feedback loops beyond solely human annotation.
Evaluation Benchmarks and Stress Testing
To measure hallucinations, GPT-5 is rigorously stress-tested on new public and internal factuality benchmarksâsuch as LongFact (concepts and objects) and FActScore (fact-seeking prompts). The evaluation framework targets harder, open-ended prompts and long-form content, areas in which hallucinations previously flourished. According to OpenAI, "GPT-5 thinking" produces about six times fewer hallucinations than o3 on these tasks.
GPT-5 is also evaluated in real-world production traffic and specialized test sets, where its ability to correctly admit knowledge gaps and avoid fabrications is directly measured and improved. For example, the model's refusal to invent non-existent assets in multimodal settings has improved markedly compared to earlier generations.
Architectural and Training Interventions
Several deeper interventions during training target hallucinations:
- Chain-of-thought prompting and structured reasoning are built into pre-training and fine-tuning phases, enabling the model to produce more explainable and grounded outputs rather than confident conjectures.
- Safe completions paradigm replaces the older refusal-based safety model, training GPT-5 to provide helpful, bounded responsesâor to transparently communicate its limits and reasoning when it cannot safely answer.
- Tool Use and Retrieval-Augmented Generation (RAG): GPT-5 is systematically trained to leverage web search and external fact-checking tools for queries that require up-to-date or highly specific knowledge. This drastically reduces the risk of hallucinations on obscure or fast-evolving subjects.
- Sycophancy reduction: GPT-5's curation pipeline explicitly gathers data designed to trap models in âagreementâ errors, scoring answers for sycophancy and using these scores as a negative reward during RLHF, directly attacking the âhallucination by agreementâ problem.
Real-World Results and Limitations
Despite these advances, GPT-5 is not fully immune to hallucinations. For instance:
- The reported hallucination rate for complex, open-ended tasks (measured by benchmarks like Simple QA) remains significant, especially when the system is cut off from live fact-checking tools.
- Access to web search reduces error rates considerably, illustrating the importance of hybrid training (combining static curated data with retrieval) in moderating hallucinations.
- Certain creative or abstract prompts continue to challenge the system's grounding mechanisms.
Continuous Updates and Community Feedback
GPT-5's system is fed ongoing community and real-user data, with feedback mechanisms that allow for quick patching of discovered hallucinations and rollout of refinements in both data filtering and reward function design. OpenAI openly acknowledges the need for further improvement, especially in high-stakes domains like healthcare and law, where error tolerance must be minimal.
Summary of Key Curation Steps
To synthesize, the reduction of hallucinations in GPT-5 arises from the following interlinked processes:
1. Meticulous pre-training data selection and filtering, with an emphasis on sourcing from reputable databases and maintaining up-to-date factual content.
2. Exclusion of noisy, unreliable, or biased content during dataset assembly, reinforced by automated and manual review at multiple stages.
3. Reinforcement learning and continuous feedback based on large-scale human and automated grading for factuality and truthfulness.
4. Evaluation against robust factuality benchmarks, both static and real-world, measuring the precise rate and type of hallucinations under various conditions.
5. Post-training interventions, including safer completion strategies, explicit sycophancy suppression, and strong integration with retrieval or tool-based knowledge.
6. Iterative live tuning from production feedback and red-teaming, ensuring new âleakagesâ of hallucinations are quickly detected and addressed.