configs/tango.yaml

Abstract: "Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. Given the recent popularity and adoption of language generation technologies, the potential to further marginalize this population only grows. Although a multitude of NLP fairness literature focuses on illuminating and addressing gender biases, assessing gender harms for TGNB identities requires understanding how such identities uniquely interact with societal gender norms and how they differ from gender binary-centric perspectives. Such measurement frameworks inherently require centering TGNB voices to help guide the alignment between gender-inclusive NLP and whom they are intended to serve. Towards this goal, we ground our work in the TGNB community and existing interdisciplinary literature to assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation (OLG). This social knowledge serves as a guide for evaluating popular large language models (LLMs) on two key aspects: (1) misgendering and (2) harmful responses to gender disclosure. To do this, we introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community. We discover a dominance of binary gender norms reflected by the models; LLMs least misgendered subjects in generated text when triggered by prompts whose subjects used binary pronouns. Meanwhile, misgendering was most prevalent when triggering generation with singular they and neopronouns. When prompted with gender disclosures, TGNB disclosure generated the most stigmatizing language and scored most toxic, on average. Our findings warrant further research on how TGNB harms manifest in LLMs and serve as a broader case study toward concretely grounding the design of gender-inclusive AI in community voices and interdisciplinary literature."
Applicable Models:
- GPT-2
- GPT-Neo
- OPT
Authors: Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta
Considerations: Based on automatic evaluations of the resulting open language generation, may be sensitive to the choice of evaluator. Would advice for a combination of perspective, detoxify, and regard metrics
Datasets: https://huggingface.co/datasets/AlexaAI/TANGO
Group: CulturalEvals
Hashtags: .nan
Link: '“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation'
Modality: Text
Screenshots:
- Images/TANGO1.png
- Images/TANGO2.png
Suggested Evaluation: Human and Toxicity Evals of Cultural Value Categories
Level: Output
URL: http://arxiv.org/abs/2106.10328
What it is evaluating: Bias measurement for trans and nonbinary community via measuring gender non-affirmative language, specifically 1) misgendering 2), negative responses to gender disclosure
Metrics: .nan
Affiliations: .nan
Methodology: .nan