Papers with profession
Big AI is Accelerating the Metacrisis: What Can We Do? (2026.acl-short)
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| Challenge: | LLM engineering is at the core of the problem of ecological, meaning, and language crises . big AI is fueling global crises and creating wealth and power for a handful of individuals and corporations while causing existential harm to life on earth. |
| Approach: | et al., 2025, p162ff) argue that big AI is escalating global crises and creating a metacrisis. |
| Outcome: | the field of natural language processing is at the core of the problem . it is being leveraged to create unprecedented wealth and power for a handful of individuals and corporations while causing existential harm to life on earth. |
A Trip Towards Fairness: Bias and De-Biasing in Large Language Models (2024.starsem-1)
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| Challenge: | a little or a large bias in CtB-LLMs may cause huge harm . LLaMA and OPT families have an important bias in gender, race, religion, and profession. |
| Approach: | They propose to debiase three families of Very Large-Language Models with LORA to reduce bias by 4.12 points in the normalized stereotype score. |
| Outcome: | The proposed model reduces bias up to 4.12 points in the normalized stereotype score. |
BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla (2025.findings-acl)
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| Challenge: | ***BanStereoSet*** is a dataset designed to evaluate stereotypical social biases in multilingual LLMs for the Bangla language. |
| Approach: | They propose to localize the content from StereoSet, IndiBias, and kamruzzaman-etal's datasets to capture biases prevalent within the Bangla language. |
| Outcome: | The proposed dataset consists of 1,194 sentences spanning 9 categories of bias: race, profession, gender, ageism, beauty, beauty in profession, region, caste, and religion. |
StereoSet: Measuring stereotypical bias in pretrained language models (2021.acl-long)
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| Challenge: | Existing literature on stereotypical biases in language models is limited . current evaluations focus on measuring bias without considering language modeling ability . |
| Approach: | They propose to measure stereotypical biases in four domains: gender, profession, race, and religion . they compare stereotypical and language modeling ability of popular models like BERT, GPT-2, RoBERTa and XLnet . |
| Outcome: | The proposed model shows strong stereotypical biases in gender, profession, race, and religion domains. |
Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
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| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
| Approach: | They propose a favorable debiasing method that uses sensitive information ‘fairly’, rather than blindly eliminating it. |
| Outcome: | The proposed method achieves a trade-off between debiasing and task performance along with producing debiased rationales as evidence. |
Toward Deconfounding the Effect of Entity Demographics for Question Answering Accuracy (2021.emnlp-main)
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| Challenge: | Existing question answering datasets lack diversity in gender, profession, and nationality. |
| Approach: | They focus on how well QA models generalize across demographic subsets . english-language QA datasets mostly ask about US men from a few professions - this is problematic because most English speakers are not from the US or UK . |
| Outcome: | The proposed model accuracy is lower for people based on gender, profession, and nationality, but there is more variation on professions (question topic) and question ambiguity. |
Stereotype Detection as a Catalyst for Enhanced Bias Detection: A Multi-Task Learning Approach (2025.findings-acl)
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| Challenge: | a new study addresses bias and stereotypes in language models by exploring how learning them together improves performance. |
| Approach: | They propose a dataset for bias and stereotype detection that integrates religion, gender, socio-economic status, race, profession, and others. |
| Outcome: | The proposed dataset compares encoder-only models and fine-tuned decoder- only models . the results show that learning stereotypes together improves bias detection . |
An Empirical Analysis of the Writing Styles of Persona-Assigned LLMs (2024.emnlp-main)
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| Challenge: | Recent efforts to "personalize" large language models by assigning them specific personas are limited by current knowledge of how well they perform. |
| Approach: | They use a style embedding model to analyze writing styles of persona-assigned LLMs . they find significant style differences between personas using Kullback-Leibler divergence . |
| Outcome: | The proposed model shows significant differences in writing styles among personas across socio-demographic groups. |