Papers by Jihwan Seol
VoiceBBQ: Investigating Effect of Content and Acoustics in Social Bias of Spoken Language Model (2025.emnlp-main)
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| Challenge: | Due to the nature of speech modality, social bias in Spoken Language Models (SLMs) can emerge from two distinct sources: 1) content aspect and 2) acoustic aspect. |
| Approach: | They propose a dataset that measures social bias by presenting ambiguous or disambiguated contexts followed by questions that may elicit stereotypical responses. |
| Outcome: | The proposed dataset converts every BBQ context into controlled voice conditions, enabling per-axis accuracy, bias, and consistency scores comparable to the original text benchmark. |