Knowledge cutoff
 Knowledge cutoffs of popular LLMs | |
| Field | Artificial intelligence, Machine learning |
|---|---|
| Key people | Research groups at OpenAI, Anthropic, Google AI |
| Purpose | The point in time beyond which a model has not been trained on new data. |
In machine learning, a knowledge cutoff (or data cutoff) is the date that marks the end of the data used for a model's training, especially for a large language model (LLM).[1] Any information about events after this date is absent from the model's internal knowledge base. A model's knowledge is static after this date. It cannot access information about later events without a system for real-time data access, such as RAG.[2] While useful for training and tuning LLMs, knowledge cutoffs introduce new limitations like hallucinations, information gaps and temporal bias.[1] To mitigate these issues, methods like RAG and continual learning are used to supplement static knowledge with dynamic or updated information.[2]
Overview
[edit]A model with a fixed knowledge cutoff is unable to provide information on facts or developments that have emerged since that time. Notable model cutoff dates include:
- The GPT-4 model has a knowledge cutoff of September 2021.[3]
- The GPT-4 Turbo model has a knowledge cutoff of December 2023.[3]
Factors behind knowledge cutoffs
[edit]Using a static dataset is a core requirement for the reproducible evaluation of a model's performance. The practice is also reinforced by the high financial and computational cost of retraining large language models.[4] The complexity of data-gathering pipelines also introduces a natural delay, which complicates the use of real-time data.[5]
Implications and limitations
[edit]Knowledge gaps
[edit]Knowledge cutoffs create information gaps. The model lacks any knowledge of events, discoveries, or cultural shifts that postdate its training data.[6] This can lead to hallucinations, where the model generates plausible but verifiably false statements. Such inaccuracies occur because LLMs are optimized for linguistic plausibility, not factual correctness.[7]
Temporal bias
[edit]Training data from a specific period reflects the social norms, terminology and ethical views of that era. A model's responses can therefore fail to align with current societal values as time passes, resulting in temporal bias.[8]
Effective vs. reported cutoffs
[edit]Research indicates a model's functional knowledge may not be uniformly limited by its stated cutoff date. This "effective" cutoff often differs for various subjects and is influenced by the distribution of information within the training data itself.[1][9] Some models can also use integrated search tools to access more recent information, which blurs the line of their inherent knowledge base. For example, modern versions of ChatGPT like GPT-4o can access its search tool and give real time info.
Attempts to overcome knowledge cutoffs
[edit]Retrieval-augmented generation
[edit]Retrieval-augmented generation (RAG) is a common technique used to overcome the limitations of a static knowledge cutoff.[2] In a RAG system, the language model is connected to an external knowledge base or search engine to pull in live data. This architecture allows the model to find current information relevant to a query and incorporate it into its response, often with citations.[2] Grounding a model in external data helps reduce the frequency of hallucinations and improves output accuracy. However, the external knowledge base might be outdated or contain biases, which deeply affects the LLM.[10]
Continual learning
[edit]Another approach is continual learning, which involves methods like adapters and LoRA. [11] These fine-tuning techniques permit efficient, incremental updates to a model without the high cost of a full retraining cycle. However, this does not give real-time awareness, as it requires rapid manual tuning to solve the issue, which is not feasible.[11]
Controversies and criticisms
[edit]Techniques like RAG have their own limitations. They can perform poorly on complex queries in specialized fields such as law or finance.[12] The output quality is also dependent on the retrieved information; if the external data is biased or inaccurate, the model's response will reflect those flaws.[10]
See also
[edit]- Retrieval-augmented generation
- Continual learning
- Language model
- Hallucination (artificial intelligence)
- Algorithmic bias
References
[edit]- ^ a b c Haji, Fatemeh; Bethany, Mazal; Tabar, Maryam; Chiang, Jason; Rios, Anthony; Najafirad, Peyman (2024). "Dated Data: Tracing Knowledge Cutoffs in Large Language Models". arXiv:2409.11527 [cs.AI].
- ^ a b c d Martineau, Kim (22 August 2023). "What is retrieval-augmented generation (RAG)?". IBM Research. Retrieved 24 July 2025.
- ^ a b Lee, Gordon (6 December 2023). "Paid ChatGPT users can now access GPT-4 Turbo". Engadget. AOL. Retrieved 27 July 2025.
- ^ Henshall, Will (3 June 2024). "The Billion-Dollar Price Tag of Building AI". TIME. Retrieved 24 July 2025.
- ^ "Top Challenges in Real-Time Data Pipelines and How to Solve Them". CelerData. 30 January 2025. Retrieved 24 July 2025.
- ^ "Can ChatGPT discuss current events? The chatbot's knowledge cutoff date". Fox News. 28 June 2023. Retrieved 2025-07-24.
- ^ "How does ChatGPT work? AI explained with a simple diagram". NBC News. 17 May 2023. Retrieved 2025-07-24.
- ^ Stevens, Lisa M. (2021). "Temporal bias in case-control design: preventing reliable predictions of the future". Nature Communications. 12 (1) 1107. Bibcode:2021NatCo..12.1107Y. doi:10.1038/s41467-021-21390-2. PMC 7889612. PMID 33597541.
- ^ Cheng, Jeffrey; Marone, Marc; Weller, Orion; Lawrie, Dawn; Khashabi, Daniel; Benjamin Van Durme (2024). "Effective Knowledge Cutoffs in Large Language Models". arXiv:2403.12958 [cs.CL].
- ^ a b "Why are Google's AI Overviews results so bad?". MIT Technology Review. Retrieved 2025-07-24.
- ^ a b "CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning". CVPR 2025 Open Access Repository. Computer Vision Foundation. 2025. Retrieved 24 July 2025.
- ^ Qian, Hongjin; Liu, Zheng (2025). "InForage: Probing Information Foraging Behaviors in Retrieval-Augmented Language Models". arXiv:2505.09316 [cs.CL].