Mikolov-style analogy arithmetic (d̂ = b − a + c) applied to already-trained embeddings, scored by how well the predicted point's nearest neighbor recovers the true fourth term, against a random-chance baseline — a direct empirical readout of concept-crystal structure rather than a theoretical derivation of it.
Used in (7 observations)
structure: Linear Direction · models: 1-layer, 1-head causal Transformer (d_model=128, synthetic entity-relation analogy task) · paper: Emergent Analogical Reasoning in Transformers
structure: Linear Direction · models: wav2vec 2.0 Large (LV-60), HuBERT Large (LibriLight 60k), WavLM Large · paper: [b]=[d]-[t]+[p]: Self-Supervised Speech Models Discover Phonological Vector Arithmetic
structure: Linear Direction · models: GPT-J-6B, GPT-2 Small, GPT-2 Medium, GPT-2 Large, GPT-2 XL, BLOOM-176B · paper: Language Models Implement Simple Word2Vec-style Vector Arithmetic
structure: Linear Direction · models: RNNLM word vectors (Mikolov, Yih & Zweig 2013, Broadcast News) · paper: Linguistic Regularities in Continuous Space Word Representations
structure: Linear Direction · models: DCGAN (trained on aligned & cropped celebrity faces) · paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
structure: Concept Crystals (Parallelogram/Trapezoid Structure) · models: SMI-TED289M, MolFormer (100M molecules) · paper: A Large Encoder-Decoder Family of Foundation Models For Chemical Language
structure: Concept Crystals (Parallelogram/Trapezoid Structure) · models: Wav2Vec 2.0 Base (LibriSpeech-960h) · paper: Emergent morpho-phonological representations in self-supervised speech models