Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models acquire general knowledge from pretraining but pretraining data contain undesirable social biases which can be perpetuated or even amplified by LLMs. |
| Approach: | They propose an efficient yet effective annotation pipeline to investigate social biases in pretraining data. |
| Outcome: | The proposed pipeline investigates social biases in the pretraining corpus using protected attribute detection and regard classification. |
Similar Papers
A Predictive Factor Analysis of Social Biases and Task-Performance in Pretrained Masked Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Various types of social biases have been reported with pretrained Masked Language Models (MLMs) in prior work. |
| Approach: | They conduct a comprehensive study on 39 pretrained MLMs to examine their model factors and their social biases. |
| Outcome: | The proposed model factors influence social biases learned by an MLM and their downstream task performance. |
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023.acl-long)
Copied to clipboard
| Challenge: | Hundreds of studies have highlighted ethical issues in NLP models . |
| Approach: | They propose to measure media biases in LMs trained on diverse data sources . they focus on hate speech and misinformation detection . |
| Outcome: | The proposed methods quantify the fairness of downstream NLP models trained on politically biased LMs. |
Social Bias in Multilingual Language Models: A Survey (2025.emnlp-main)
Copied to clipboard
| 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. |
Debiasing Large Language Models with Structured Knowledge (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
| Approach: | They propose a method that utilizes structured knowledge to mitigate bias in LLMs . their method obviates the need for training from scratch, thus offering enhanced scalability . |
| Outcome: | The proposed method outperforms state-of-the-art (SOTA) baselines in the debiasing ability. |
Understanding the Effect of Model Compression on Social Bias in Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) trained with self-supervision on vast corpora of web text fit to the social biases of that text, leading to representational harm. |
| Approach: | They propose to use quantization and knowledge distillation to reduce the computational burden of LLMs to mitigate the effects of inappropriate social biases learned during pretraining. |
| Outcome: | The proposed methods reduce the computational burden of large language models by reducing their size and complexity. |
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
| Approach: | They analyze the impact of 48 datasets from 5 major categories of pretraining data of Large Language Models and measure their impacts on LLMs using benchmarks about nine major categories. |
| Outcome: | The proposed analysis provides insights into the organization of data to support more efficient pretraining of Large Language Models. |
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models use cultural biases implicitly, causing harm . identifying and quantifying learnt biase enables us to measure progress . |
| Approach: | They propose a benchmark to measure social bias in pretrained language models . they use 1508 examples that cover stereotypes dealing with nine types of bias . |
| Outcome: | The proposed benchmark focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. |
BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization (2024.acl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been shown to be effective in complex language tasks, but their potential to perpetuate biases poses significant concerns. |
| Approach: | They propose a new framework employing Direct Preference Optimization to mitigate biases in LLMs. |
| Outcome: | The proposed model outperforms the baseline model on almost all bias benchmarks and achieves better performance than open-source models. |
A Study of Implicit Bias in Pretrained Language Models against People with Disabilities (2022.coling-1)
Copied to clipboard
| Challenge: | Pretrained language models exhibit sociodemographic biases, such as against gender and race, raising concerns of downstream biase in language technologies. |
| Approach: | They propose to use word embedding-based and transformer-based PLMs to test for the presence of biases against people with disabilities (PWDs) |
| Outcome: | The proposed models favor ableist language, despite their sociodemographic biases against race and gender. |
Biases in Large Language Model-Elicited Text: A Case Study in Natural Language Inference (2025.coling-main)
Copied to clipboard
| Challenge: | Creating NLP datasets with Large Language Models (LLMs) is an attractive alternative to relying on crowd-source workers. |
| Approach: | They recreate a portion of the Stanford Natural Language Inference corpus using GPT-4, Llama-2 70b for Chat, and Mistral 7b Instruct. |
| Outcome: | The proposed model can be used to generate NLP datasets with stereotypical biases and annotation artifacts. |