Papers with mitigating bias
ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness (2024.lrec-main)
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| Challenge: | Recent studies in Natural Language Processing (NLP) have unveiled a concerning issue: stereotypical biases associated with demographic groups are prevalent. |
| Approach: | They propose an approach that actively encodes stereotypical biases into the embedding space by integrating stereotypes into a model that acquires agency and belief scores rather than directly representing stereotypes. |
| Outcome: | The proposed model can learn agency and belief stereotypes while preserving the language model’s proficiency. |
Social-Group-Agnostic Bias Mitigation via the Stereotype Content Model (2023.acl-long)
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Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, Morteza Dehghani
| Challenge: | Existing methods for mitigating bias require social-group-specific word pairs for each social attribute (e.g., gender) Existing approaches require only one social attribute, rendering them impractical and costly . |
| Approach: | They propose that stereotype content models capture the underlying connection between bias and stereotypes by embedding only two psychological dimensions of warmth and competence. |
| Outcome: | The proposed method performs comparably to group-specific debiasing on multiple bias benchmarks, but has theoretical and practical advantages over existing methods. |
Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration (2025.naacl-long)
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Ang Li, Jingqian Zhao, Bin Liang, Lin Gui, Hui Wang, Xi Zeng, Xingwei Liang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Large language models generate biased stances due to spurious correlations and preference towards certain individuals and topics. |
| Approach: | They propose a counterfactual Augmented Calibration Network to calibrate potential bias in stance detection of large language models. |
| Outcome: | The proposed calibration network can mitigate biases of large language models, achieving state-of-the-art results. |
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection (2023.findings-acl)
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| Challenge: | Natural language processing models tend to learn and encode social biases present in the data. |
| Approach: | They propose a method for removing non-linear encoded concepts from neural representations by iteratively training neural classifiers to predict a particular attribute, followed by a projection of the representation on a hypersurface. |
| Outcome: | The proposed method removes non-linear encoded concepts from neural representations. |
What’s in a Name? Reducing Bias in Bios without Access to Protected Attributes (N19-1)
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Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai
| Challenge: | Existing methods for mitigating bias in machine learning systems rely on access to protected attributes such as race, gender, or age. |
| Approach: | They propose a method for discouraging correlation between predicted probability of an individual’s true occupation and a word embedding of their name. |
| Outcome: | The proposed method reduces race and gender biases, with almost no reduction in the classifier’s overall true positive rate. |
A Group Fairness Lens for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods focusing on a few groups lack a comprehensive categorical perspective to evaluate LLMs’ potential biases and unfairness. |
| Approach: | They propose to evaluate LLM biases from a group fairness lens using a hierarchical schema characterizing diverse social groups. |
| Outcome: | The proposed method mitigates biases in LLMs from a group fairness lens and encapsulates target-attribute combinations across multiple dimensions. |
KLAAD: Refining Attention Mechanisms to Reduce Societal Bias in Generative Language Models (2025.emnlp-main)
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| Challenge: | Large language models exhibit societal biases in their outputs, prompting ethical and societal challenges. |
| Approach: | They propose an attention-based debiasing framework that implicitly aligns attention distributions between stereotypical and anti-stereotypical sentence pairs without directly modifying model weights. |
| Outcome: | The proposed framework improves on BBQ and BOLD benchmarks while maintaining fluency and coherence. |
Social Bias Evaluation for Large Language Models Requires Prompt Variations (2025.findings-emnlp)
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| Challenge: | Recent studies have tried to evaluate and mitigate social biases accurately using limited prompts. |
| Approach: | They investigate the sensitivity of Large Language Models when changing prompt variations . they found that LLM rankings fluctuate across prompts for both task performance and social bias . |
| Outcome: | The results show that LLM rankings fluctuate when changing prompt variations . |
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, but their effectiveness in ECI remains limited due to biases in causal reasoning. |
| Approach: | They propose a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities to help LLM models in ECI. |
| Outcome: | The proposed framework leverages LLMs’ few-shot learning capabilities to guide LLM models in causal reasoning, mitigating bias and improving accuracy. |