Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models

[2408.07665] Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2408.07665 (cs) [Submitted on 14 Aug 2024] Title: Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models Authors: Yi-Cheng Lin , Wei-Chih Chen , Hung-yi Lee View a PDF of the paper titled Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models, by Yi-Cheng Lin and 2 other authors View PDF HTML (experimental) Abstract: Warning: This paper may contain texts with uncomfortable content. Large Language Models (LLMs) have achieved remarkable performance in various tasks, including those involving multimodal data like speech. However, these models often exhibit biases due to the nature of their training data. Recently, more Speech Large Language Models (SLLMs) have emerged, underscoring the urgent need to address these biases. This study introduces Spoken Stereoset, a dataset specifically designed to evaluate social biases in SLLMs. By examining how different models respond to speech from diverse demographic groups, we aim to identify these biases. Our experiments reveal significant insights into their performance and bias levels. The findings indicate that while most models show minimal bias, some still exhibit slightly stereotypical or anti-stereotypical tendencies. Subjects: Computation and Language (cs.CL) ; Audio and Speech Processing (eess.AS) Cite as: arXiv:2408.07665 [cs.CL] (or arXiv:2408.07665v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2408.07665 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2024 IEEE Spoken Language Technology Workshop (SLT), Macao, 2024, pp. 871-878 Related DOI : https://doi.org/10.1109/SLT61566.2024.10832259 Focus to learn more DOI(s) linking to related resources Submission history From: Yi-Cheng Lin [ view email ] [v1] Wed, 14 Aug 2024 16:55:06 UTC (31 KB) Full-text links: Access Paper: View a PDF of the paper titled Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models, by Yi-Cheng Lin and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-08 Change to browse by: cs eess eess.AS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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