Aleph Alpha Luminous Explain

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When preprocessing input documents, it is important to take the different formats into account. In machine-readable PDFs, the text can be extracted along with exact coordinates. For example, the same position of information in two documents does not necessarily mean that it is the same information. Tables in PDFs can also be presented in different ways and are therefore difficult to recognize. Processing scans or images is even more difficult because the text cannot be extracted directly from the image. OCR technologies aim to automatically recognize text in image files or scanned documents and convert it into digital, editable text formats.

An alternative option is to use multimodal models. These AI systems simultaneously process and integrate information from different modalities, such as text and images. By combining and understanding different sources of information, they enable a more comprehensive analysis and interpretation of data. Examples of such systems are Aleph Alpha's Magma or GPT-4. However, these models are not specialized in text, which is why the use of an OCR system can be useful, as it is specifically optimized for the recognition of texts - whether handwritten or machine-written.


Successfully implementing language models for information indonesia consumer email list retrieval requires careful planning and optimization. One important factor is the design of the input prompt. Language models used for information retrieval purposes are often not as human-oriented as chat models. Therefore, it can be helpful to design the prompt, i.e. the input to the model, to match the model's specific "language". For example, a system message could read: "You are a bot that is an expert for extracting information. But you only speak JSON."

A possible prompt could then be formulated as follows: “Please extract me the following information from the text: [,price', ,amount', ,name of customer', ,address of customer'].” The most effective approach to optimizing prompt design should be iterative, testing and adjusting different variants to achieve the best possible results.

Another important aspect is the cleaning and optimization of the text fed to the model. Since almost every character in a language model is considered a token, excessive whitespace and line breaks can cause unnecessary costs.

Another optimization is few shots. Simply put, this is about giving the model examples that help it better understand the task. That is, in the document processing scenario, in addition to the instructions, you would give the model an example, e.g. an invoice and the data to be extracted.


The following describes the advantages and disadvantages as well as possible problems and solutions for different approaches to information extraction.