Paper
Same Words, Different Judgments: Modality Effects on Preference Alignment
Authors
Aaron Broukhim, Nadir Weibel, Eshin Jolly
Abstract
Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences, but its application to speech remains underexplored. We present a controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts. Audio preferences prove as reliable as text, with inter-rater agreement reaching good levels (ICC(2,k) $\approx$ .80) at $\sim$9 raters -- the first ICC-based reliability characterization in the preference annotation literature for either modality. However, modality reshapes how people judge: audio raters exhibit narrower decision thresholds, reduced length bias, and more user-oriented evaluation criteria, with near-chance cross-modality agreement. Synthetic ratings further align with human judgments and predict inter-rater agreement, supporting their use both for triaging ambiguous pairs and as full replacements for human annotations.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22710v1</id>\n <title>Same Words, Different Judgments: Modality Effects on Preference Alignment</title>\n <updated>2026-02-26T07:34:15Z</updated>\n <link href='https://arxiv.org/abs/2602.22710v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22710v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences, but its application to speech remains underexplored. We present a controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts. Audio preferences prove as reliable as text, with inter-rater agreement reaching good levels (ICC(2,k) $\\approx$ .80) at $\\sim$9 raters -- the first ICC-based reliability characterization in the preference annotation literature for either modality. However, modality reshapes how people judge: audio raters exhibit narrower decision thresholds, reduced length bias, and more user-oriented evaluation criteria, with near-chance cross-modality agreement. Synthetic ratings further align with human judgments and predict inter-rater agreement, supporting their use both for triaging ambiguous pairs and as full replacements for human annotations.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.SD'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.HC'/>\n <published>2026-02-26T07:34:15Z</published>\n <arxiv:comment>Submitted to Interspeech 2026 for review</arxiv:comment>\n <arxiv:primary_category term='cs.SD'/>\n <author>\n <name>Aaron Broukhim</name>\n </author>\n <author>\n <name>Nadir Weibel</name>\n </author>\n <author>\n <name>Eshin Jolly</name>\n </author>\n </entry>"
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