LV-Eval

LV-Eval is a challenging long-context benchmark with five length levels (16k, 32k, 64k, 128k, and 256k) reaching up to 256k words. The average number of words is 102,380, and the Min/Max number of words is 11,896/387,406. LV-Eval features two main tasks, single-hop QA and multi-hop QA, comprising 11 bilingual datasets. The design of LV-Eval has incorporated three key techniques, namely confusing facts insertion (CFI), keyword and phrase replacement (KPR), and keyword-recall-based metrics (AK, short for metics with Answer Keywords and word blacklist) design, which jointly provide a challenging, mitigated-knowledge-leakege, and more accurate evaluation of the long-context capability of LLMs. We anticipate that LV-Eval will serve as a valuable resource for supporting future research on long-context LLMs.