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有人问 Laya 和 Jev 的准确度到底差多少，我自己又跑了一轮小测试。 这次还是用 Laya 英文版，一共 100 道判断题： Laya：92 正确，8 错误 Jev：100 正确 在这组测试里，Jev 高出 8%。 当然，这不是完整 benchmark，只能说明在这批偏“理解和判断”的题目里，两者表现有差异。 我主要测了几个容易拉开差距的场景： 1、属性特征描述（Feature Alignment） 例： The sweet red fruit with a leafy green cap on top. 不直接说名字，只给特征描述。 测试模型能不能理解“红色 + 甜 + 顶部绿色叶子”这些条件组合，而不是只匹配关键词。 2、单重 / 双重否定（Negation） 例： Skip the grapes and citrus, give me the other one. 很多小模型容易抓住高频关键词： grapes、citrus 但忽略前面的 skip。 这里考验的是模型能不能正确处理否定和排除条件。 3、上下文干扰和目标指代（Mention vs Intent） 例： The recipe picture has grapes, but the fruit to serve is orange. 前面提到了 grapes，但真正要选择的是 orange。 测试模型能不能区分

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码农暖爸@Delroy715𝕏
有人问 Laya 和 Jev 的准确度到底差多少，我自己又跑了一轮小测试。 这次还是用 Laya 英文版，一共 100 道判断题： Laya：92 正确，8 错误 Jev：100 正确 在这组测试里，Jev 高出 8%。 当然，这不是完整 benchmark，只能说明在这批偏“理解和判断”的题目里，两者表现有差异。 我主要测了几个容易拉开差距的场景： 1、属性特征描述（Feature Alignment） 例： The sweet red fruit with a leafy green cap on top. 不直接说名字，只给特征描述。 测试模型能不能理解“红色 + 甜 + 顶部绿色叶子”这些条件组合，而不是只匹配关键词。 2、单重 / 双重否定（Negation） 例： Skip the grapes and citrus, give me the other one. 很多小模型容易抓住高频关键词： grapes、citrus 但忽略前面的 skip。 这里考验的是模型能不能正确处理否定和排除条件。 3、上下文干扰和目标指代（Mention vs Intent） 例： The recipe picture has grapes, but the fruit to serve is orange. 前面提到了 grapes，但真正要选择的是 orange。 测试模型能不能区分
Sep 23, 2026X postsView on X

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