Papers by Md Asiful Islam
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
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| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
| Outcome: | The proposed methods perform poorly on the few-shot relation extraction task. |
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification (2024.findings-naacl)
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| Challenge: | a novel neuro-symbolic architecture for relation classification combines rule-based methods with deep learning techniques. |
| Approach: | They propose a neuro-symbolic architecture for relation classification that combines rule-based methods with deep learning techniques. |
| Outcome: | The proposed approach outperforms state-of-the-art models in three out of four settings . human interventions boost the performance on the relation org:parents by as much as 26% relative improvement . |
A Lightweight Explainable Guardrail for Prompt Safety (2026.acl-long)
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| Challenge: | Existing methods to detect unsafe prompts are based on reinforcement learning with human feedback (RLHF) or direct preference optimization (DPO) . however, these methods lack explainability and are costly to implement. |
| Approach: | They propose a lightweight explainable guardrail method to detect unsafe prompts using a multi-task learning architecture and a novel strategy to counteract confirmation biases. |
| Outcome: | The proposed method obtains equivalent or better performance than the state-of-the-art for both prompt classification and explainability on three datasets. |