| Challenge: | Existing studies have shown that nationality biases in language models can be a factor in improving the performance of social NLP models. |
| Approach: | They propose to use a text generation model, GPT-2, to analyze how the number of internet users and the country’s economic status affects the sentiment of stories. |
| Outcome: | The proposed model accentuates biases about country-based demonyms and reduces them with the use of adversarial triggering. |
Similar Papers
Quite Good, but Not Enough: Nationality Bias in Large Language Models - a Case Study of ChatGPT (2024.lrec-main)
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| Challenge: | Nationality is a key demographic element that enhances the performance of large language models, but it has received less scrutiny regarding inherent biases. |
| Approach: | They investigated nationality bias in ChatGPT, a large language model for text generation. |
| Outcome: | The proposed model generates 4,680 discourses about nationality in Chinese and English, with 195 countries, 4 temperature settings, and 3 prompt types. |
Towards Controllable Biases in Language Generation (2020.findings-emnlp)
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| Challenge: | a new method to induce societal biases in natural language generation is being developed . a method to equalize the amount of biased text across demographics is effective . |
| Approach: | They propose a method to induce societal biases in natural language generation by using demographic inequalities. |
| Outcome: | The proposed method is effective at equalizing biases across demographics while generating less negatively biased text overall. |
Identifying Bias in Machine-generated Text Detection (2026.acl-long)
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| Challenge: | a growing number of generative AI systems are detecting text generated by a model or written by . humans perform poorly at the detection task, but show no significant biases on the studied attributes. |
| Approach: | They examine gender, race/ethnicity, English-language learner status, and economic status . they find several models tend to classify disadvantaged groups as machine-generated . |
| Outcome: | The proposed models show strong performance but can cause negative impacts . the models classify disadvantaged groups as machine-generated, while economically disadvantaged students' essays are less likely to be classified as machine generated . |
Societal Biases in Language Generation: Progress and Challenges (2021.acl-long)
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| Challenge: | Language generation techniques can produce undesirable societal biases that can negatively impact marginalized populations. |
| Approach: | They propose to examine how decoding techniques contribute to biases in language generation . they also conduct experiments to quantify the effects of these techniques . |
| Outcome: | The proposed methods can reduce biases and improve user experience, the authors argue . they also show that the proposed techniques can reduce societal biase . |
“Fifty Shades of Bias”: Normative Ratings of Gender Bias in GPT Generated English Text (2023.emnlp-main)
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| Challenge: | Prior work treats gender bias as a binary classification task, but a comparative annotation framework can be used to assess the impact of biases. |
| Approach: | They propose to generate a dataset with normative ratings of gender bias in English text with a comparative annotation framework. |
| Outcome: | The first dataset of GPT-generated English text with normative ratings of gender bias is analyzed using Best–Worst Scaling . |
Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables (2024.lrec-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
| Approach: | They propose a method to detect biases of a specific fine-tuned classifier on unlabeled data. |
| Outcome: | The proposed method detects biases on unlabeled data on named entity perturbations . it uses name-entity recognition on target-domain data and morphosynctactically different languages spoken in relation to countries of the target groups . |
Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2020.findings-emnlp)
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Po-Sen Huang, Huan Zhang, Ray Jiang, Robert Stanforth, Johannes Welbl, Jack Rae, Vishal Maini, Dani Yogatama, Pushmeet Kohli
| Challenge: | Language modeling has advanced rapidly due to efficient model architectures and the availability of large text corpora. |
| Approach: | They propose to embed and regularize sentiment prediction-derived regularizations on the language model’s latent representations to reduce bias in the sentiment of generated text. |
| Outcome: | The proposed methods reduce bias in the sentiment of generated text by adopting individual and group fairness metrics from the fair machine learning literature. |
Richer Output for Richer Countries: Uncovering Geographical Disparities in Generated Stories and Travel Recommendations (2025.findings-naacl)
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| Challenge: | a large body of work examines language models for biases concerning gender, race, occupation and religion . however, the impact of the encoded geographical knowledge on real-world applications has not been documented . |
| Approach: | They examine large language models for two common scenarios that require geographical knowledge: travel recommendations and geo-anchored story generation. |
| Outcome: | The results show that the language models are biased against poorer countries and poorer socioeconomic conditions. |
The Woman Worked as a Babysitter: On Biases in Language Generation (D19-1)
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| Challenge: | a systematic study of biases in natural language generation (NLG) is presented . a study of language models in NLG is conducted by examining language models. |
| Approach: | They propose a systematic study of biases in natural language generation by analyzing text generated from prompts that contain mentions of different demographic groups. |
| Outcome: | The proposed method reveals biases in natural language generation (NLG) by analyzing text generated from demographic prompts. |