“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models (2021.emnlp-main)
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| Challenge: | Several studies have identified such linguistic classes of words that occur frequently in natural language text and are bias-inducing by virtue of their framing effects. |
| Approach: | They propose to use linguistic cues to induce subtle biases through implied sentiment and presupposed facts to influence the distribution of the generated text. |
| Outcome: | The proposed models are sensitive to these framing effects, but show that they lead to measurable style and topic differences in the generated text, leading to language that is, on average, more polarised and more skewed towards controversial entities and events. |
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| Challenge: | Language has enormous power to project social biases and reinforce stereotypes on people. |
| Approach: | They propose a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and power differentials onto others. |
| Outcome: | The proposed model can model the pragmatic frames in which people project social biases and power differentials onto others. |
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 . |
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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. |
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Unmasking Style Sensitivity: A Causal Analysis of Bias Evaluation Instability in Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to assess social biases in natural language processing models show unexpected instability when input texts undergo minor stylistic changes. |
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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. |
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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. |
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Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
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Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020.acl-main)
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| Challenge: | 146 papers analyzing "bias" in NLP systems lack normative reasoning, we find . authors propose three recommendations for work analyzing “bias” in Nlp systems . |
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Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain (2023.emnlp-main)
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| Challenge: | a reviewer’s opinion of the nativeness of expression in an academic paper affects the likelihood of it being accepted for publication. |
| Approach: | They conduct a statistical analysis of paper abstracts from the natural language processing domain to identify how authors from different linguistic backgrounds differ in the lexical, morphological, syntactic and cohesive aspects of their writing. |
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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 . |
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