On Measuring Social Biases in Prompt-Based Multi-Task Learning (2022.findings-naacl)
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| Challenge: | a large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance. |
| Approach: | They propose a large-scale text-to-text language model trained using prompts . they consider two different forms of semantically equivalent inputs - question-answer format and premise-hypothesis format . |
| Outcome: | The proposed model can generalize into novel forms of language and handle novel tasks. |
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| Challenge: | Existing prompt-based methods for debiasing are often superficial and lack a thorough understanding of complex bias concepts. |
| Approach: | They analyze a BBQ and stereoSet benchmarks to examine the assumption that large language models understand biases. |
| Outcome: | The proposed model misclassified 90% of unbiased content as biased despite high accuracy on BBQ dataset . the proposed model may have been flawed in previous attempts to debiase . |
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 . |
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Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)
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| Challenge: | Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction. |
| Approach: | They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations. |
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Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Pre-trained language models can encode unfair social biases from large pre-training corpora and even amplify biase in downstream applications. |
| Approach: | They propose a *debias-while-prompt tuning* method for mitigating biases via counterfactual contrastive prompt tuning on downstream tasks. |
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Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection (2025.naacl-long)
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Maximilian Spliethöver, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik, Barbara Hammer, Eyke Hüllermeier, Henning Wachsmuth
| Challenge: | Existing prompting techniques for large language models depend on several parameters, such as the task, language model, and context provided. |
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Using Natural Sentence Prompts for Understanding Biases in Language Models (2022.naacl-main)
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| Challenge: | Recent work has shown that language models are susceptible to biases present in the training dataset. |
| Approach: | They propose to use natural sentence prompts to analyze gender-occupation biases in language models. |
| Outcome: | The proposed dataset can be used to analyze gender-occupation biases in language models. |
BBQ: A hand-built bias benchmark for question answering (2022.findings-acl)
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Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, Samuel Bowman
| Challenge: | NLP models learn social biases, but little work has been done on how these biase manifest in outputs for applied tasks like question answering (QA). |
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Measuring bias in Instruction-Following models with P-AT (2023.findings-emnlp)
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| Challenge: | Instruction-Following Language Models (IFLMs) are promising and versatile tools for solving many downstream, information-seeking tasks. |
| Approach: | They propose a resource to test whether IFLMs are prone to biases . they cast WEAT word tests in promptized classification tasks and associate a metric - the bias score . |
| Outcome: | The proposed resource consists of 2310 prompts and tests gender and race biases in all the analyzed models. |
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 . |
| Approach: | They draw on a measurement modelling framework to create a bias taxonomy . they show that bias tests are often unstated or ambiguous, carry implicit assumptions . |
| Outcome: | The proposed taxonomy shows that bias tests are often unstated or ambiguous . the analysis illuminates the scope of possible bias types the field can measure . |
Bias after Prompting: Persistent Discrimination in Large Language Models (2025.findings-emnlp)
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Nivedha Sivakumar, Natalie Mackraz, Samira Khorshidi, Krishna Patel, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff
| Challenge: | a dangerous assumption is that biases do not transfer from pre-trained large language models to adapted models. |
| Approach: | They validate the bias transfer hypothesis by using prompt adaptations to study biases in causal models . they find that popular prompt-based mitigation methods do not consistently prevent biase transferring . |
| Outcome: | The results invalidate the assumption that biases do not transfer from pre-trained models to adapted models. |