Papers with mitigation approach
Prompt Perturbation Consistency Learning for Robust Language Models (2024.findings-eacl)
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Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan
| Challenge: | Large language models have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. |
| Approach: | They propose a method to reduce the performance drop of large language models by regularizing the divergence between losses from clean and perturbed samples. |
| Outcome: | The proposed approach recovers on average 59% and 69% of the performance drop for IC and SF tasks while using ten times fewer augmented data samples. |
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing work has explored unimodal biases in visual question answering, but the problem of selection bias in Multiple-Choice Question Answering (MCQA) remains underexplored. |
| Approach: | They propose a method that mitigates bias without retraining and is compatible with frozen LVLMs. |
| Outcome: | The proposed method mitigates bias without retraining and is compatible with frozen LVLMs. |