---
title: "The University of Adelaide — works of 2026 — 5 papers"
description: "5 works of The University of Adelaide from 2026 in our library, the most cited first. Among them: MMCLIP: Cross-Modal Attention Masked…"
canonical: "https://research.elamur.ai/free/en/universities/the-university-of-adelaide/year/2026/"
---
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# Works of The University of Adelaide from 2026

5 works · the most cited first · [← The University of Adelaide](https://md.research.elamur.ai/free/en/universities/the-university-of-adelaide/)

1. **[MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training](https://md.research.elamur.ai/free/en/papers/wu-2026-mmclip-cross-modal-attention-masked/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Biao Wu, Yutong Xie, Zeyu Zhang, et al.*2026 · 3 citations*
2. **[Gender Disparities in LLM-Based Intimate Partner Violence Detection](https://md.research.elamur.ai/free/en/papers/prama-2026-gender-disparities-llm-based-intimate/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, et al.*2026*
3. **[Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts](https://md.research.elamur.ai/free/en/papers/ren-2026-learning-moral-diversity-modelling-individual/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Yi Ren, Lewis Mitchell, Matthew Roughan*2026*
4. **[TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-triex-game-based-tri-view/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyi Wang, Chen Zhang, Wenjun Peng, et al.*2026*
5. **[Trustworthy and Explainable Causal Representation Learning in Transformers](https://md.research.elamur.ai/free/en/papers/liu-2026-trustworthy-explainable-causal-representation-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Liu, Yinghao Zhang, Lin Liu, et al.*2026*
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