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Llama (Large Language Model Meta AI, formerly stylized as LLaMA) is a family of large language models (LLMs) released by Meta AI starting in February 2023. [2] [3] The latest version is Llama 3.3, released in December 2024. [4] Llama models are trained at different parameter sizes, ranging between 1B and 405B. [5]
llama.cpp is an open source software library that performs inference on various large language models such as Llama. [3] It is co-developed alongside the GGML project, a general-purpose tensor library.
A translation memory (TM) is a database that stores "segments", which can be sentences, paragraphs or sentence-like units (headings, titles or elements in a list) that have previously been translated, in order to aid human translators.
Machine translation can have difficulty with individual words or expressions for many reasons, including false friends, false cognates, literal translation, neologisms, slang, idiomatic expressions and other unrecognized compounds, words with multiple meanings, words with specialized meanings in certain knowledge domains different from a common ...
With proper terminology work, with preparation of the source text for machine translation (pre-editing), and with reworking of the machine translation by a human translator (post-editing), commercial machine-translation tools can produce useful results, especially if the machine-translation system is integrated with a translation memory or ...
Various computer based translation companies were also launched, including Trados (1984), which was the first to develop and market Translation Memory technology (1989), though this is not the same as MT. The first commercial MT system for Russian / English / German-Ukrainian was developed at Kharkov State University (1991).
A translator using static binary translation aims to convert all of the code of an executable file into code that runs on the target architecture without having to run the code first, as is done in dynamic binary translation. This is very difficult to do correctly, since not all the code can be discovered by the translator.
One of the constituent parts of the ALPAC report was a study comparing different levels of human translation with machine translation output, using human subjects as judges. The human judges were specially trained for the purpose. The evaluation study compared an MT system translating from Russian into English with human translators, on two ...