The paper aims to address the issue of discrimination in language models towards queer identities, particularly focusing on the harm caused by sentence completions generated by English large language models (LLMs) concerning LGBTQIA+ individuals . This problem is not entirely new, as existing studies have explored biases in LLMs, but the specific focus on the discrimination and harm towards LGBTQIA+ individuals is a relatively underexplored area . The paper introduces the QueerBench assessment framework to evaluate the discriminatory behavior of language models towards the LGBTQIA+ community, highlighting the need to mitigate harmful biases and promote inclusivity in natural language processing .

This paper aims to validate the scientific hypothesis that large language models (LLMs) exhibit discriminatory behavior more frequently towards individuals within the LGBTQIA+ community, potentially causing harm through sentence completions generated by these models . The analysis conducted using the QueerBench assessment framework, which includes a template-based approach and a Masked Language Modeling (MLM) task, indicates a significant difference gap of 7.2% in the QueerBench score of harmfulness towards LGBTQIA+ individuals .

The paper "QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities" introduces several innovative ideas, methods, and models in the field of natural language processing (NLP) . Here are some key contributions:

1. Assessment Metrics: The paper proposes the use of three distinct techniques - AFINN, HurtLex tools, and Perspective API - to evaluate language models' predictions at completion-level and sentence-level. These tools are utilized to assess sentiment analysis, harmfulness, and toxicity in the predictions made by the language models .

2. Models: The paper utilizes various Language Model Models (LLMs) from the HuggingFace library, such as BERT, ALBERT, and RoBERTa, to evaluate biases and discrimination in language models. These models are chosen based on their domains, settings, and training datasets, focusing on their ability to perform Masked Language Modeling (MLM) tasks .

3. QueerBench Pronouns: The study examines the performance of different language models across pronoun categories, highlighting the challenges faced by natural language models in comprehending and effectively using gender-neutral pronouns like "they/them" or neo-pronouns such as "xe/xem", "ze/zir", or "fae/faer" .

4. Dataset and Nouns Categorization: The paper introduces a dataset that includes sentences structured around various identities, sexual orientations, and marginalized communities. It categorizes subjects into specific nouns and pronouns, expanding the list of nouns related to gender identity, sexual orientation, and queer culture. This dataset is used to assess biases and discrimination in language models .

Overall, the paper presents a comprehensive approach to quantifying discrimination in language models towards queer identities by employing innovative assessment metrics, utilizing diverse language models, and creating specialized datasets for evaluation. The paper "QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities" introduces innovative assessment metrics and models compared to previous methods, offering distinct characteristics and advantages:

1. Assessment Metrics:

   • The paper utilizes AFINN and HurtLex tools to evaluate language models' predictions at completion-level and Perspective API for sentence-level assessment. These tools enable sentiment analysis, harmfulness, and toxicity evaluation in language models' predictions .
   • The integration of these assessment metrics allows for a comprehensive evaluation of language models' biases towards queer identities, providing a more nuanced understanding of the discriminatory tendencies present in the models .

2. Models:

   • The study employs various Language Model Models (LLMs) from the HuggingFace library, including BERT, ALBERT, and RoBERTa, chosen based on their domains, settings, and training datasets for Masked Language Modeling (MLM) tasks .
   • By utilizing a range of LLMs, the paper enhances the evaluation of biases and discrimination in language models towards queer identities, offering a diverse and comprehensive analysis compared to previous methods .

3. Dataset and Nouns Categorization:

   • The paper introduces a specialized dataset categorizing subjects into specific nouns and pronouns related to gender identity, sexual orientation, and queer culture. This dataset expands the list of nouns and pronouns, enabling a more detailed assessment of biases in language models .
   • By categorizing nouns and pronouns based on queer contexts and conducting further research to enrich the dataset, the paper enhances the evaluation of language models' discriminatory behavior towards queer identities, providing a more nuanced analysis compared to previous methods .

Overall, the paper's innovative assessment metrics, diverse selection of models, and specialized dataset contribute to a more thorough and detailed analysis of discrimination in language models towards queer identities, offering significant advancements and insights compared to previous methods in the field of natural language processing.

Several related research studies exist in the field of language models and bias towards queer identities. Noteworthy researchers in this area include Hossain et al. (2023), Lauscher et al. (2022), Felkner et al. (2022), Devinney et al. (2022), Nozza et al. (2022b), Ousidhoum et al. (2021), and many others . These researchers have highlighted the challenges faced by natural language models in comprehending and effectively using gender-neutral pronouns and addressing biases related to queer identities.

The key to the solution mentioned in the paper involves developing template-based methods and conducting testing on a novel dataset inspired by the work of Ousidhoum et al. (2021) . This approach includes creating a dataset with sentences structured as "PersonX ACTION because he [MASK]", where "PersonX" represents various attributes related to marginalized communities, genders, sexual orientations, and social groups . By employing a keyword substitution method similar to "PersonX", the study aims to diversify the case study across different subjects to address biases in language models towards queer identities.

The experiments in the paper were designed with a structured approach involving three key steps :

1. Dataset Creation: The researchers generated a set of meaningful sentences by combining subjects with neutral sentences through a Masked Language Model (MLM) task.
2. Generate Predictions: Each neutral sentence was combined with each subject to create complete meaningful sentences, which were then input into various Language Models (LMs) like BERT, ALBERT, RoBERTa, and BERTweet. The models provided predictions for the MLM task, with consideration given to both "base" and "large" versions. The predictions were evaluated for connotation, harmfulness, and toxicity at both word and sentence levels using AFINN, HurtLex, and Perspective API tools.
3. Evaluate Predictions: The researchers aggregated the results from the different models to derive a single QueerBench score, which assessed the performance and potential biases of the language models towards queer identities.

The dataset used for quantitative evaluation in the study is comprised of 8,268 complete and meaningful sentences created by combining a set of subjects with neutral sentences through the MLM task . The code for the QueerBench framework is not explicitly mentioned as open source in the provided context.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study conducted various tests on language models, particularly focusing on the discrimination towards queer identities . The experiments included Perspective tests on ALBERT models, AFINN and HurtLex tools assessments, and evaluations of model predictions related to pronouns and nouns categories .

The results of the experiments revealed significant insights into the behavior of the language models towards queer identities. For instance, the models exhibited elevated levels of toxicity for sentences with queer nouns as subjects, indicating a difference of approximately 5% across all models . Additionally, the HurtLex test showed that predictions falling into the queer category were assessed as more harmful compared to other categories, with toxicity levels reaching up to 15-20% in large models .

Moreover, the study highlighted the challenges faced by natural language models in comprehending and effectively using gender-neutral pronouns like "they/them" or neo-pronouns, which are crucial for inclusivity . The research also emphasized the importance of addressing biases in language models to create an equitable and inclusive digital environment, especially for the LGBTQIA+ community .

Overall, the experiments and results presented in the paper offer valuable insights and empirical evidence to support the scientific hypotheses related to discrimination in language models towards queer identities. The findings contribute to the ongoing research efforts aimed at understanding and mitigating biases in natural language processing models, particularly concerning LGBTQIA+ individuals .

The paper "QueerBench: Quantifying Discrimination in Language Models Toward Queer Identities" makes several significant contributions:

• It assesses the potential harm caused by sentence completions generated by English large language models concerning LGBTQIA+ individuals using the QueerBench assessment framework, which employs a template-based approach and a Masked Language Modeling (MLM) task .
• The study reveals that language models tend to exhibit discriminatory behavior more frequently towards individuals within the LGBTQIA+ community, with a difference gap of 7.2% in the QueerBench score of harmfulness .
• It highlights the bias present in considered language models, showing that sentences with queer subjects exhibit an average harmfulness percentage of 16.9%, while sentences with non-queer subjects demonstrate an average harmfulness of 9.2% .
• The paper emphasizes the consequences of language technologies that exclude specific genders, perpetuating discrimination against underrepresented and marginalized groups, and advocates for the promotion of inclusivity, respect, and equality within artificial intelligence and natural language processing .
• Furthermore, it calls for continued efforts to improve language models to mitigate harmful biases for everyone, regardless of their sexual orientation or gender identity, to foster a more inclusive society and advance research in assessing and mitigating bias in language models .

Further research in the field of Natural Language Processing (NLP) can be expanded by delving deeper into the complexities of queer identities and associated biases. Studies like those by Hossain et al. (2023) and Lauscher et al. (2022) have highlighted the challenges faced by natural language models in effectively understanding and utilizing gender-neutral pronouns like "they/them" or neo-pronouns such as "xe/xem", "ze/zir", or "fae/faer" . Additionally, there is a need to incorporate gender theory more explicitly in NLP research, with a focus on intersectionality and inclusion, especially concerning non-binary genders . This would contribute to a more comprehensive understanding of biases in large language models (LLMs) and their impact on queer communities.