SHARE: Social-Humanities AI for Research and Education

arXiv cs.CL / 4/14/2026

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Key Points

  • The article introduces SHARE, a family of base causal language models designed and fully pretrained specifically for social sciences and humanities (SSH), alongside a companion MIRROR user interface for SSH-focused workflows.
  • It claims SHARE’s performance on SSH text is close to that of a general-purpose model (Phi-4) despite using roughly 100 times fewer tokens, evaluated via a custom SSH Cloze benchmark.
  • The MIRROR interface is intended to support critical review of SSH text inputs while preserving disciplinary engagement and norms.
  • The report proposes a textless generative AI interaction pattern (via MIRROR) to leverage SHARE model capabilities without generating new text that could undermine SSH principles.

Abstract

This intermediate technical report introduces the SHARE family of base models and the MIRROR user interface. The SHARE models are the first causal language models fully pretrained by and for the social sciences and humanities (SSH). Their performance in modelling SSH texts is close to that of general purpose models (Phi-4) which use 100 times more tokens, as shown by our custom SSH Cloze benchmark. The MIRROR user interface is designed for reviewing text inputs from the SSH disciplines while preserving critical engagement. By prototyping a generative AI interface that does not generate any text, we propose a way to harness the capabilities of the SHARE models without compromising the integrity of SSH principles and norms.