LLMs implement word2vec-style relational vector offsets, causally patchable

Merullo et al. show many one-to-one relations (e.g. country-capital) that LLMs solve in-context are implemented as a simple additive vector offset computed by a mid-network FFN update [merullo-etal-2024-language-models-implement-word2vec-style-vector-arithmetic] This is directly analogous to word2vec-style vector arithmetic but discovered inside real pretrained transformer activations rather than static embeddings [merullo-etal-2024-language-models-implement-word2vec-style-vector-arithmetic] Extracting the offset from one example context and patching it into another causally forces the model to output the corresponding relation's correct answer for the new subject [merullo-etal-2024-language-models-implement-word2vec-style-vector-arithmetic] The mechanism is replicated across GPT-J, GPT-2 (small through XL), and BLOOM (up to 176B parameters) [merullo-etal-2024-language-models-implement-word2vec-style-vector-arithmetic]