@inproceedings{yang-etal-2026-voice,
title = "Voice of {A}ustralian-{AI}: Learning {A}ustralian Assistant Voice Without Caricature",
author = "Yang, Yue and
Twelftree, Maxwell and
Borgnolo, Luke and
Lemphers, David and
Baughan, Amanda",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.114/",
pages = "1944--1961",
ISBN = "979-8-89176-460-6",
abstract = "LLM assistants deployed outside the United States often inherit US-centred interactional defaults. We introduce an Australian-facing assistant, and test whether local voice is better learned during fine-tuning or imposed with an inference-time prompt. We define Australian Voice as pragmatic alignment, directness, modesty, warmth, register control, and task fidelity, rather than slang or spelling substitution. We build a governed dataset from user research, human rewrites, contrastive negatives, reviewed synthetic examples, and replay data. In a blind evaluation by Australian English users (356 question-level ratings over 89 prompts), the raw 50{\%} voice/replay mix scores best on all five dimensions (4.24/5; 102 best-pick wins). Prompted variants underperform every matched fine-tuned model."
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<abstract>LLM assistants deployed outside the United States often inherit US-centred interactional defaults. We introduce an Australian-facing assistant, and test whether local voice is better learned during fine-tuning or imposed with an inference-time prompt. We define Australian Voice as pragmatic alignment, directness, modesty, warmth, register control, and task fidelity, rather than slang or spelling substitution. We build a governed dataset from user research, human rewrites, contrastive negatives, reviewed synthetic examples, and replay data. In a blind evaluation by Australian English users (356 question-level ratings over 89 prompts), the raw 50% voice/replay mix scores best on all five dimensions (4.24/5; 102 best-pick wins). Prompted variants underperform every matched fine-tuned model.</abstract>
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%0 Conference Proceedings
%T Voice of Australian-AI: Learning Australian Assistant Voice Without Caricature
%A Yang, Yue
%A Twelftree, Maxwell
%A Borgnolo, Luke
%A Lemphers, David
%A Baughan, Amanda
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F yang-etal-2026-voice
%X LLM assistants deployed outside the United States often inherit US-centred interactional defaults. We introduce an Australian-facing assistant, and test whether local voice is better learned during fine-tuning or imposed with an inference-time prompt. We define Australian Voice as pragmatic alignment, directness, modesty, warmth, register control, and task fidelity, rather than slang or spelling substitution. We build a governed dataset from user research, human rewrites, contrastive negatives, reviewed synthetic examples, and replay data. In a blind evaluation by Australian English users (356 question-level ratings over 89 prompts), the raw 50% voice/replay mix scores best on all five dimensions (4.24/5; 102 best-pick wins). Prompted variants underperform every matched fine-tuned model.
%U https://aclanthology.org/2026.emnlp-industry.114/
%P 1944-1961
Markdown (Informal)
[Voice of Australian-AI: Learning Australian Assistant Voice Without Caricature](https://aclanthology.org/2026.emnlp-industry.114/) (Yang et al., EMNLP 2026)
ACL