LV-Eval is a bilingual long-context benchmark designed to evaluate large language models at very large context lengths (up to 256k tokens). It provides controllable evaluation across five length levels (16k, 32k, 64k, 128k, 256k) and includes multiple QA-style tasks (single-hop and multi-hop QA) drawn from several bilingual datasets. The benchmark incorporates techniques to reduce knowledge leakage and increase difficulty and objectivity: confusing facts insertion (CFI), keyword and phrase replacement (KPR), and a keyword-recall-based metric evaluated at multiple lengths. LV-Eval is provided with balanced numbers of instances across lengths and is intended to stress-test long-context capabilities of LLMs.
Accuracy is the reported evaluation metric for LV-Eval. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.
Higher is better
| Rank | Model | Trust | Score | Year | Source |
|---|---|---|---|---|---|
| 01 | Qwen2.5-72B-Instruct | paper | 60.4 | N/A | Source ↗ |