| 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. |
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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 . |
Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text (2026.findings-acl)
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| Challenge: | Large language models generate demographically conditioned persuasive texts at scale . authors argue that such capabilities raise questions about fairness and representational bias in automated communication. |
| Approach: | They propose a framework for evaluating demographic-conditioned targeted messages . they find gender- and age-based asymmetries in male- and youth-targeted messages a . |
| Outcome: | The proposed framework evaluates generated messages across three dimensions: lexical content, language style, and persuasive framing. |
Quantifying Bias from Decoding Techniques in Natural Language Generation (2022.coling-1)
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| Challenge: | Natural language generation (NLG) models can propagate social bias towards particular demography. |
| Approach: | They propose to examine whether bias metrics like toxicity and sentiment are impacted by decoding techniques that use stochastic decoding. |
| Outcome: | The proposed methods reveal the imperative of testing inference time bias and provide evidence on the usefulness of inspecting the entire decoding spectrum. |
Nationality Bias in Text Generation (2023.eacl-main)
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| 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. |
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 . |
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2020.acl-main)
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| Challenge: | a growing number of studies address the effect of bias on predictions, but no unifying framework exists . a general phenomenon of biased predictive models in NLP is not recent, authors say . |
| Approach: | They propose a unifying framework for identifying and reducing bias in natural language processing . they propose to differentiate two consequences of bias and four potential origins of bias . |
| Outcome: | The proposed framework provides an overview of predictive bias in natural language processing . it differentiates two consequences of bias and four potential origins of bias: label bias, selection bias, model overamplification, and semantic bias. |
Mitigating Societal Harms in Large Language Models (2023.emnlp-tutorial)
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| Challenge: | Recent studies have highlighted societal harms that can be caused by language generation models deployed in the wild. |
| Approach: | They propose to use a typology of technical approaches to mitigating harms of language generation models to provide an overview of potential social issues in language generation including toxicity, social biases, misinformation, factual inconsistency, and privacy violations. |
| Outcome: | The proposed typology addresses toxicity, biases, misinformation, factual inconsistency, and privacy violations in language generation models. |
Balancing out Bias: Achieving Fairness Through Balanced Training (2022.emnlp-main)
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| Challenge: | Existing approaches to reducing group bias do not account for correlations between author demographics and linguistic variables, limiting their effectiveness. |
| Approach: | They extend a method for countering group bias using balanced training by balancing each demographic group in training and using protected attributes as input. |
| Outcome: | The proposed model outperforms all other methods when combined with balanced training. |
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. |
A Simple, Yet Effective Approach to Finding Biases in Code Generation (2023.findings-acl)
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
| Approach: | They propose a framework that automatically identifies subtle cues a code generation model might exploit . they propose an automated intervention mechanism reminiscent of adversarial testing . |
| Outcome: | The proposed framework can be used as a data transformation technique during fine-tuning, acting as reversal strategy. |