The Semantic Hub Hypothesis: Language Models Share Semantic
  Representations Across Languages and Modalities
Authors: name: Zhaofeng Wu, name: Xinyan Velocity Yu, name: Dani Yogatama, name: Jiasen Lu, name: Yoon Kim

Modern language models can process inputs across diverse languages and
modalities. We hypothesize that models acquire this capability through learning
a shared representation space across heterogeneous data types (e.g., different
languages and modalities), which places semantically similar inputs near one
another, even if they are from different modalities/languages. We term this the
semantic hub hypothesis, following the hub-and-spoke model from neuroscience
(Patterson et al., 2007) which posits that semantic knowledge in the human
brain is organized through a transmodal semantic "hub" which integrates
information from various modality-specific "spokes" regions. We first show that
model representations for semantically equivalent inputs in different languages
are similar in the intermediate layers, and that this space can be interpreted
using the model's dominant pretraining language via the logit lens. This
tendency extends to other data types, including arithmetic expressions, code,
and visual/audio inputs. Interventions in the shared representation space in
one data type also predictably affect model outputs in other data types,
suggesting that this shared representations space is not simply a vestigial
byproduct of large-scale training on broad data, but something that is actively
utilized by the model during input processing.