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Separation of information extraction and logical inference: strategies for complex data sets

In practice, the use of large language models often fails due to the efficient processing of extensive contexts. The sheer volume of data does not automatically lead to higher decision quality; the opposite is often the case. The correct selection of data as a foundation for reliable decisions therefore represents a strategic challenge with regard to the use of artificial intelligence in the context of large language models.

The problem: signal overlay and attention deficits

Current scientific studies show that language models tend to overlook crucial facts if they are lost in a flood of irrelevant or outdated information. The “lost in the middle” effect is particularly critical. Information that is neither at the beginning nor at the end of a text is processed less reliably by the model.

The primary problem lies in the conflation of two distinct functions of the artificial neural network within a single computational process: the filtering of task-critical text passages from the overall noise, and the drawing of inferences based on the identified facts.

The attention mechanisms of current language models often provide unreliable results when the ratio of useful information to noise data is poor, so that logical precision suffers from inadequate preselection.

The solution: modular decoupling of AI systems

To ensure processing quality, a specialized, resource-saving module can be installed upstream instead of a universal system, which acts as a filter and only identifies the relevant text sections. A powerful language model is then used for the logical evaluation of the pre-selected, relevant data.

Strategic relevance for corporate use

This architecture model offers several significant advantages for operational use. For example, this model does not require access to the internal computing processes of the models. This means that closed programming interfaces from external providers can also be used without losing control over data preparation.

The architecture model described provides explicit textual evidence for the conclusions reached. This option makes the LLM’s decision-making basis comprehensible and controllable for humans and creates an important basis for audit security through source transparency in the sense of the AI Act.

The identification of relevant information follows the logical structure of the data, rather than the specific idiosyncrasies of a particular model. Consequently, once-optimized filtering strategies can be directly transferred to new model generations, while technological dependency is simultaneously reduced. Below (or alongside) is an exemplary implementation within the DookuSense application by Open Logic Systems (Open LS).

Questions about information extraction and logical reasoning?