A primary criticism towards language models (LMs) is their inscrutability.
This paper presents evidence that, despite their size and complexity, LMs
sometimes exploit a computational mechanism familiar from traditional word
embeddings: the use of simple vector arithmetic in order to encode abstract
relations (e.g., Poland:Warsaw::China:Beijing). We investigate a range of
language model sizes (from 124M parameters to 176B parameters) in an in-context
learning setting, and find that for a variety of tasks (involving capital
cities, upper-casing, and past-tensing), a key part of the mechanism reduces to
a simple linear update applied by the feedforward networks. We further show
that this mechanism is specific to tasks that require retrieval from
pretraining memory, rather than retrieval from local context. Our results
contribute to a growing body of work on the mechanistic interpretability of
LLMs, and offer reason to be optimistic that, despite the massive and
non-linear nature of the models, the strategies they ultimately use to solve
tasks can sometimes reduce to familiar and even intuitive algorithms.

本文研究了语言模型在上下文学习中的机制和解决问题的策略，发现虽然它们规模巨大和复杂，但其实有时候它们会利用简单向量算术的机制编码抽象关系，并且利用前馈网络的简单线性更新算法来完成各种任务，在一定程度上具有可解释性。

语言模型实现简单的 Word2Vec 风格的向量算术

Language Models Implement Simple Word2Vec-style Vector Arithmetic

A surprising property of word vectors is that word analogies can often be
solved with vector arithmetic. However, it is unclear why arithmetic operators
correspond to non-linear embedding models such as skip-gram with negative
sampling (SGNS). We provide a formal explanation of this phenomenon without
making the strong assumptions that past theories have made about the vector
space and word distribution. Our theory has several implications. Past work has
conjectured that linear substructures exist in vector spaces because relations
can be represented as ratios; we prove that this holds for SGNS. We provide
novel justification for the addition of SGNS word vectors by showing that it
automatically down-weights the more frequent word, as weighting schemes do ad
hoc. Lastly, we offer an information theoretic interpretation of Euclidean
distance in vector spaces, justifying its use in capturing word dissimilarity.

研究发现，使用向量算术操作可以解决单词类比问题，本文提供了 skip-gram 模型中算术运算符对应的非线性嵌入模型的正式解释，证明了线性子结构存在于向量空间中，通过从技术上的角度证明了降低高频词权重的有效性以及欧氏距离在捕捉单词不相似性问题上的信息论解释。