Knowledge Cutoff

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). 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. This concept started with the release of GPT-3 in 2020. Major labs like Google, OpenAI and Anthropic began publicly disclosing cutoff dates for transparency. While useful for training and tuning LLMs, knowledge cutoffs introduce new limitations like hallucinations, information gaps and temporal bias. To mitigate these issues, methods like RAG and continual learning are used to supplement static knowledge with dynamic or updated information.

Overview

Training large language models on static datasets is standard practice. This is necessary for achieving reproducibility and stability in performance evaluation. A model with a fixed knowledge cutoff is therefore unable to provide information on facts or developments that have emerged since that time. Notable model cutoff dates include:
* GPT-3 (released June 2020) has a knowledge cutoff of June 2019. The GPT-3.5 model's cutoff is September 2021.
* The GPT-4 model has a knowledge cutoff of September 2021; its GPT-4 Turbo variant is updated to December 2023. GPT-4o has a primary cutoff of October 2023 but can access more recent information.
* The Claude 3 models have a knowledge cutoff of August 2023. The later Claude 3.5 Sonnet has a cutoff of April 2024.
* Gemini 1.5 Pro has a knowledge cutoff of at least November 2023, though some newer versions have later dates.

Historical context

Early large language models like BERT (2018) and T5 (2019) were also trained on fixed datasets, but the companies did not typically state an explicit knowledge cutoff date. The practice of announcing a cutoff date became an industry standard for transparency after the release of GPT-3 in 2020. Other major AI labs like Anthropic and Google later adopted this procedure.

Factors behind knowledge cutoffs

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 models. The complexity of data-gathering pipelines also introduces a natural delay, which complicates the use of real-time data.

Implications and limitations

Knowledge gaps

Knowledge cutoffs create information gaps. The model lacks any knowledge of events, discoveries, or cultural shifts that postdate its training data. 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, and attempt to fill these knowledge gaps.

Temporal bias

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.

Effective vs. reported cutoffs

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. 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

Retrieval-augmented generation

Retrieval-augmented generation (RAG) is a common technique used to overcome the limitations of a static knowledge cutoff. 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. Grounding a model in external data helps reduce the frequency of hallucinations and improves output accuracy. However, the external knowledge base may be outdated and contain biases, which deeply affects the LLM.

Continual learning

Another approach is continual learning, which involves methods like adapters and LoRA. 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.

Controversies and criticisms

Techniques like RAG have their own limitations. They can perform poorly on complex queries in specialized fields such as law or finance. 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. A broader critique against LLMs is that they lack genuine comprehension and instead function as advanced pattern-matching systems.

See also

* Retrieval-augmented generation
* Continual learning
* Language model
* Hallucination (artificial intelligence)
* Algorithmic bias

References

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