5 papers
Efficient Safety Guardrails through Latent Dual-Hypothesis Reasoning with On-Demand Trace Reconstruction
Md Asiful Islam, Fahmida Alam, Mihai Surdeanu
Guardrail approaches that rely on reasoning-based large language models (LLM) improve safety decisions, but come with substantial inference overhead due to the explicit reasoning.…
A Dual-Hypothesis Reasoning Framework for LLM Guardrails
Md Asiful Islam, Mihai Surdeanu, Fahmida Alam
We propose ARBITER, a novel LLM guardrail framework that introduces two key ideas: (i) dual-hypothesis reasoning, a reasoning method for LLM guardrails that explicitly considers bo…
A Lightweight Explainable Guardrail for Prompt Safety
Md Asiful Islam, Mihai Surdeanu
We propose a lightweight explainable guardrail (LEG) method to detect unsafe prompts. LEG uses a multi-task learning architecture to jointly learn a prompt classifier and an explan…
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation
Fahmida Alam, Md Asiful Islam, Robert Vacareanu +1
We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019…
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
Robert Vacareanu, Fahmida Alam, Md Asiful Islam +2
This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approac…