Analyzing the Limits of Self-Supervision in Handling Bias in Language (2022.findings-emnlp)
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| Challenge: | a recent study shows that natural language models can perform tasks with little to no in-context supervision . a number of tasks are performed using self-supervised pre-training . |
| Approach: | They define and comprehensively evaluate how well natural language taskprompting captures the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. |
| Outcome: | The proposed model performs to wide varying degrees across bias dimensions . the model is largely challenged when prompted to perform these tasks . |
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Mind the Biases: Quantifying Cognitive Biases in Language Model Prompting (2023.findings-acl)
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| Challenge: | Cognitive biases in the human decision making process can lead to flawed responses when we are under uncertainty. |
| Approach: | They propose to expose cognitive biases on results of language model prompting which display bias modes resembling cognitive bias. |
| Outcome: | The proposed methods show that a toning-down transformation of the drug-drug description in a prompt can elicit a bias similar to the framing effect, warning users to distrust when prompting language models for answers. |
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs (2024.naacl-long)
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| Challenge: | Large language models exhibit undesirable preference toward predicting certain answers over others, despite their adaptability to diverse tasks. |
| Approach: | They propose a label bias calibration method that outperforms recent calibration approaches for improving performance and mitigating label bias. |
| Outcome: | The proposed method outperforms calibration approaches for improving performance and mitigating label bias. |
Adapting Bias Evaluation to Domain Contexts using Generative Models (2025.emnlp-main)
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| Challenge: | Existing approaches to assess social bias in NLP systems face limitations in scalability and fidelity across domains. |
| Approach: | They propose a domain-adaptive framework that uses prompting with Large Language Models to automatically transform template-based bias datasets into domain-specific variants. |
| Outcome: | The proposed framework improves the accuracy and contextual relevance of bias evaluations in socially relevant datasets. |
Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text (2025.findings-emnlp)
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| Challenge: | Persona-prompting is a growing strategy to personalize outputs, but its impact on how LLMs represent social groups remains underexplored. |
| Approach: | They investigate whether persona-prompting leads to different levels of linguistic abstraction . they compare 11 persona driven responses to those of a generic AI assistant . |
| Outcome: | The proposed method can be used to personalize outputs, but its impact on how LLMs represent social groups remains underexplored. |
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes (2025.naacl-short)
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Isabel O. Gallegos, Ryan Aponte, Ryan A. Rossi, Joe Barrow, Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt, Nedim Lipka, Deonna Owens, Jiuxiang Gu
| Challenge: | Large language models exhibit harmful social biases, but they are often difficult to train and modify. |
| Approach: | They leverage the zero-shot capabilities of large language models to reduce stereotyping . they introduce a technique called zero- shot self-debiasing to reduce bias . |
| Outcome: | The proposed technique reduces stereotyping across nine different social groups while relying on the LLM itself and a simple prompt. |
Analyzing Effects of Learning Downstream Tasks on Moral Bias in Large Language Models (2024.lrec-main)
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| Challenge: | Existing methods for fine-tuning large language models replicate and perpetuate social biases . pre-existing moral bias may be mitigated or amplified even when presented with opposing views . |
| Approach: | They develop methods to assess the agreement of LMs to explicit codified norms . they find that introducing downstream tasks may lead to unexpected inconsistencies . |
| Outcome: | The proposed model can be used to improve morality in data-scarce tasks. |
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations. |
| Approach: | They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch. |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance. |
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 . |
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
| Outcome: | The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias. |
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. |