{"id":223,"date":"2023-07-16T03:42:34","date_gmt":"2023-07-16T03:42:34","guid":{"rendered":"https:\/\/cdn.mlnews.dev\/?p=223"},"modified":"2023-10-02T06:06:46","modified_gmt":"2023-10-02T06:06:46","slug":"fot-lama-unlocking-the-potential-of-long-context","status":"publish","type":"post","link":"https:\/\/mlnews.dev\/fot-lama-unlocking-the-potential-of-long-context\/","title":{"rendered":"Is A Long Context Sequence Achievable? FOT-LAMA Unlocking The Potential Of Long Context"},"content":{"rendered":"\n
Revolutionary language model is here to improve context length in both single and multiple documents with the help of FOT. Its main purpose is to expand context length without compromising on performance. Extending context length beyond its limitation helps it to understand and process more information. It was first published at Google DeepMind, IDEAS NCBR, the Polish Academy of Sciences, and the University of Warsaw. Multiple researchers have put effort in this research including Szymon Tworkowski<\/a>, Konrad Staniszewski<\/a>, Miko\u0142aj Pacek<\/a>, Yuhuai Wu<\/a>, Henryk Michalewski<\/a>, Piotr Mi\u0142o\u015b<\/a>. <\/p>\n\n\n\n FOT has achieved good results by implementing it in real-life scenarios. FOT is different from other models because it processes information in a different manner.<\/p>\n\n\n Previous research faced problems regarding processing longer text. They work well in some scenarios but struggle to attain long context length. Discover inaccurate results at a certain point. This response makes the system less effective. It was easy to handle context length till 2,000 tokens, but the system cannot handle tokens over this limit. This drawback causes problems in the system. When adding multiple documents, it becomes overloaded with information that starts giving irrelevant information.<\/p>\n\n\n\n Previous language models work well with short documents and hard with long pieces of documents. It was hard to process long documents and showcase the right information.<\/p>\n\n\n\n FOT has a significant impact on language models to optimize long context length. It can easily handle tokens above 2,000 and capture full lengthy passages to attain accurate results. Ultimately a new concept of memory attention layer was launched, allowing the model to accept tokens from large contexts.<\/p>\n\n\n\nExplore Challenges Of Long-Context Modeling<\/h2>\n\n\n\n
FOT overcome previous work Limitation<\/h2>\n\n\n\n