Papers with EQA datasets
Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology (2024.findings-emnlp)
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| Challenge: | Existing models lack robustness against distribution shifts and adversarial attacks when training on unanswerable questions in EQA datasets. |
| Approach: | They propose a novel loss function for the EQA problem to improve the robustness of extractive question answering models by adding adversarial questions to a crowdsourcing process. |
| Outcome: | The proposed method maintains in-domain performance while improving on out-of-domain datasets. |
Exploring Language Model Generalization in Low-Resource Extractive QA (2025.coling-main)
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| Challenge: | Existing LLMs struggle with dataset demands of closed domains such as medicine and law . current LLM performance in closed domain is lacking, even on traditional tasks such as Natural Language Inference . |
| Approach: | They investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift . they find that LLMs struggle with dataset demands of closed domains . |
| Outcome: | The proposed model performs poorly in extractive question answering tasks under domain drift . the proposed model can generalize to domains that require specific knowledge without training . |