Papers with bias evaluation
LLM BiasScope: A Real-Time Bias Analysis Platform for Comparative LLM Evaluation (2026.eacl-demo)
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| Challenge: | Existing work on bias evaluation includes benchmark datasets and automated detection methods. |
| Approach: | They propose an open-source web application for side-by-side comparison of LLM outputs with real-time bias analysis. |
| Outcome: | The open-source application compares LLM outputs with real-time bias analysis. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
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| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
| Challenge: | Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. |
| Approach: | They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups . |
| Outcome: | The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures. |
How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? (2025.naacl-long)
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| Challenge: | Recent studies have shown that LLMs exhibit social biases inherited from training data. |
| Approach: | They propose a framework for evaluation and mitigation of bias in Large Language Models applied to complex clinical cases using a dataset based on the JAMA Clinical Challenge. |
| Outcome: | The proposed framework employs multiple choice questions and explanations to evaluate gender and ethnicity biases in LLMs. |
From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation (2026.findings-acl)
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| Challenge: | Existing methods to evaluate code generation bias focus on overt discrimination through simple conditional statements. |
| Approach: | They examine ML pipelines that exhibit substantially greater bias than simple conditionals . they challenge simple conditional statements as valid proxies for bias evaluation . |
| Outcome: | The proposed model underestimates real-world bias in generating machine learning pipelines . the model maintains equal performance on simple conditionals and ML pipelines, the study shows . |
Evaluating the Effect of Retrieval Augmentation on Social Biases (2026.eacl-long)
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| Challenge: | RAG is a popular method for injecting up-to-date knowledge into LLMs. |
| Approach: | They examine how RAG modulates social biases across three languages and four categories . they find that biased documents are amplified even when base LLM has low-level of intrinsic bias . |
| Outcome: | The proposed method can enhance factual accuracy but its effect on social biases is not well understood. |
Beyond Names: How Grammatical Gender Markers Bias LLM-based Educational Recommendations (2026.eacl-long)
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| Challenge: | grammatical gender cues alone trigger substantial distributional shifts in educational recommendations . authors show that up to 76% of the bias exhibited when using prompts with proper names is already present with grammatical gender markers alone. |
| Approach: | They investigate gender biases exhibited by LLM-based virtual assistants in Italian . they show that simply changing noun and adjective endings significantly shifts recommendations . |
| Outcome: | The findings highlight the need for robust bias evaluation and mitigation strategies before deploying LLM-based virtual assistants in student-facing contexts. |
Evaluating Gender Bias of Pre-trained Language Models in Natural Language Inference by Considering All Labels (2024.lrec-main)
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| Challenge: | Existing methods to evaluate gender bias in PLMs focus on one label out of three labels, such as neutral. |
| Approach: | They propose a bias evaluation method for PLMs that considers all the three labels of NLI task and then defines a measure based on the corresponding label output. |
| Outcome: | The proposed method can distinguish biased, incorrect inferences from non-biased incorrect infertility better than baseline, resulting in a more accurate bias evaluation. |
CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models (2023.acl-long)
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| Challenge: | Existing studies on social biases in language models have focused on only English. |
| Approach: | They propose to use a Chinese dataset for bias evaluation and mitigation of Chinese conversational language models. |
| Outcome: | The proposed dataset includes under-explored bias categories, such as ageism and appearance biases, which received less attention in previous studies. |
Social Bias Probing: Fairness Benchmarking for Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating social biases in language models have been limited to binary association tests on small datasets. |
| Approach: | They propose a framework for probing language models for social biases by assessing disparate treatment . they use a large-scale benchmark to examine the diversity of identities and stereotypes . |
| Outcome: | The proposed framework expands the analysis beyond the binary comparison of stereotypical versus anti-stereotypical identities to include a diverse range of identities and stereotypes. |
Social Bias in Multilingual Language Models: A Survey (2025.emnlp-main)
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| Challenge: | Pretrained multilingual models exhibit the same social bias as models processing English texts. |
| Approach: | They examine the literature on bias evaluation and mitigation approaches in multilingual and non-English contexts and identify gaps in the field. |
| Outcome: | The proposed models perform well on multilingual language understanding benchmarks and are consistent with the current literature. |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
A Scalable Entity-Based Framework for Auditing Bias in Large Language Models (2026.findings-acl)
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| Challenge: | Existing approaches to bias evaluation in large language models trade ecological validity for statistical control, or use artificial prompts that lack scale and rigor. |
| Approach: | They propose a framework that uses named entities as probes to measure bias in large language models. |
| Outcome: | The proposed framework reproduces bias patterns observed in natural text, enabling large-scale analysis. |
A Multilingual Social Bias Benchmark Incorporating Thinking Processes (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can learn useful knowledge and harmful stereotypes, making bias evaluation essential. |
| Approach: | They propose a multilingual social bias benchmark that incorporates human-generated reasoning as part of the thinking process. |
| Outcome: | The proposed method demonstrates superior performance over LLM-generated methods . human-generated thinking yields higher-quality evaluations than template-based approaches . |