Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases (2025.emnlp-main)
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| Challenge: | a growing number of efforts to measure and mitigate gender bias have focused on task prompts that overtly or covertly signal the presence of gender bias-related content. |
| Approach: | They examine how signaling the evaluative purpose of a task impacts measured gender bias in LLMs. |
| Outcome: | The proposed models show that prompts that align with (gender bias) evaluation framing elicit distinct gender output distributions compared to less evaluation-framed prompts. |
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| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
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Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models can cause harmful, human-like biases against various demographics. |
| Approach: | They propose a causal formulation for bias measurement in generative language models based on a list of desiderata for designing robust bias benchmarks and a bias-measuring procedure to investigate occupational gender bias. |
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Bias in Language Models: Beyond Trick Tests and Towards RUTEd Evaluation (2025.acl-long)
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| Challenge: | Standard bias benchmarks are used for large language models to measure the association between social attributes and single-word outputs. |
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Job Unfair: An Investigation of Gender and Occupational Bias in Free-Form Text Completions by LLMs (2025.emnlp-main)
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| Challenge: | a recent study has identified that LLMs are used in domains where they support or replace human decision-making . a systematic review of LLM outputs shows that many facets of social bias remain unaccounted for . |
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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. |
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Evaluating Gender Bias of LLMs in Making Morality Judgements (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in a multitude of NLP tasks, but are still not immune to limitations such as gender bias. |
| Approach: | They propose to use a dataset to examine whether LLMs possess gender bias when asked to give moral opinions. |
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This prompt is measuring <mask>: evaluating bias evaluation in language models (2023.findings-acl)
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| Challenge: | a growing body of work uses prompts and templates to assess bias in language models . authors examine the scope of possible bias types and identify those under-researched . |
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Measuring Political Bias in Large Language Models: What Is Said and How It Is Said (2024.acl-long)
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| Challenge: | Existing benchmarks and measures focus on gender and racial biases, but political bias exists in LLMs and can lead to polarization and other harms in downstream applications. |
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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. |
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JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models (2024.findings-emnlp)
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Ze Wang, Zekun Wu, Xin Guan, Michael Thaler, Adriano Koshiyama, Skylar Lu, Sachin Beepath, Ediz Ertekin, Maria Perez-Ortiz
| Challenge: | a framework for benchmarking hierarchical gender hiring bias in Large Language Models (LLMs) is developed to protect vulnerable demographic groups. |
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