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  • 整体思路以及计算方式
  • 简评
  1. Arch

Generalization through Memorization: Nearest Neighbor Language Models

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Last updated 2 years ago

论文地址：

代码：

参考资料：

整体思路以及计算方式

利用Knn做模型集成，整体思路如下：

  • 记ct=(w1,…wt−1)c_t=\left(w_1, \ldots w_{t-1}\right)ct​=(w1​,…wt−1​)；

  • 假设有一个训练好的语言模型fff；

  • 我们构造如下KV数据库：

    (K,V)={(f(ci),wi)∣(ci,wi)∈D}(\mathcal{K}, \mathcal{V})=\left\{\left(f\left(c_i\right), w_i\right) \mid\left(c_i, w_i\right) \in \mathcal{D}\right\}(K,V)={(f(ci​),wi​)∣(ci​,wi​)∈D}

  • 然后利用下式构造概率分布：

    pkNN(y∣x)∝∑(ki,vi)∈N1y=viexp⁡(−d(ki,f(x)))p_{\mathrm{kNN}}(y \mid x) \propto \sum_{\left(k_i, v_i\right) \in \mathcal{N}} \mathbb{1}_{y=v_i} \exp \left(-d\left(k_i, f(x)\right)\right)pkNN​(y∣x)∝(ki​,vi​)∈N∑​1y=vi​​exp(−d(ki​,f(x)))

  • 最后模型的输出为：

    p(y∣x)=λpkNN(y∣x)+(1−λ)pLM(y∣x)p(y \mid x)=\lambda p_{\mathrm{kNN}}(y \mid x)+(1-\lambda) p_{\mathrm{LM}}(y \mid x)p(y∣x)=λpkNN​(y∣x)+(1−λ)pLM​(y∣x)

简评

  • 最后的效果是十分明显的，这里唯一的问题是，KV数据库和训练文本大小成正比，如果训练文本太大，则开销太大；

  • 另一方面Knn的作用似乎是记忆功能，所以基于Transformer的模型似乎记忆能力较弱？是否可以引入类似功能的模块提升性能；

  • 基于检索的LM是否有可行性？

https://arxiv.org/abs/1911.00172
https://github.com/urvashik/knnlm
https://zhuanlan.zhihu.com/p/90890672