{"version":"1.0","type":"card","id":"a2e80df2-d29a-4456-8fa3-6f98bc2783cc","url":"https://stacklist.com/card/a2e80df2-d29a-4456-8fa3-6f98bc2783cc","title":"Context Length Extension Techniques in LLMs — arXiv Survey","source_url":"https://arxiv.org/abs/2401.07872","note":"Deep-dives into how researchers push models beyond their training context limit — RoPE scaling, ALiBi, sliding window attention, and more. Invaluable if you want to understand why some models handle long documents better than others.","image":{"url":"https://ucarecdn.com/084b5c31-0e11-4aff-9a08-1b39bdcab85b/","alt":"Context Length Extension Techniques in LLMs — arXiv Survey","width":316,"height":96},"stack":{"id":"e16dcdcb-06b9-481c-aef9-40b6d73a5c8e","title":"Master AI & LLM Context: From Basics to Production","url":"https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e"},"created_at":"2026-07-02T10:08:42.596Z","updated_at":null,"aco":{"summary":"This survey paper explores context length extension techniques in Large Language Models, examining why extending context length is essential, the inherent challenges, and existing strategies employed by researchers. It provides an organized overview of evaluation methods, highlights open challenges, and discusses the lack of consensus on evaluation standards within the research community.","tags":["context-length-extension","large-language-models","natural-language-processing","survey","transformer-architecture","text-comprehension","evaluation-standards"],"key_entities":[{"name":"Saurav Pawar","type":"person","confidence":0.95},{"name":"S.M Towhidul Islam Tonmoy","type":"person","confidence":0.95},{"name":"S M Mehedi Zaman","type":"person","confidence":0.9},{"name":"Vinija Jain","type":"person","confidence":0.9},{"name":"Aman Chadha","type":"person","confidence":0.9},{"name":"Amitava Das","type":"person","confidence":0.9},{"name":"Large Language Models","type":"technology","confidence":1},{"name":"Context Length Extension","type":"concept","confidence":1},{"name":"Natural Language Processing","type":"concept","confidence":0.98},{"name":"arXiv","type":"organization","confidence":0.95}],"classification":"reference","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T10:08:54.093Z"},"token_counts":{"approximate":1119,"cl100k":996},"content_hash":"sha256:3e42c0dc13066e1a9ffc459ba02238aeafeb447fecee926e24a54dcddd22d17a","acp_version":"0.2","body_available":true,"body_tokens":1119,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/a2e80df2-d29a-4456-8fa3-6f98bc2783cc.json","html":"https://stacklist.com/card/a2e80df2-d29a-4456-8fa3-6f98bc2783cc","md":"/api/public/card/a2e80df2-d29a-4456-8fa3-6f98bc2783cc.md","stack_json":"/api/public/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e.json"}}