6 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…
Bidirectional Small-Granularity Search between Code and Text
Marco A. Valenzuela-Escárcega, Enrique Noriega-Atala, Gus Hahn-Powell +2
We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fra…
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…
Information Extraction from Conversation Transcripts: Neuro-Symbolic vs. LLM
Alice Saebom Kwak, Maria Alexeeva, Gus Hahn-Powell +5
The current trend in information extraction (IE) is to rely extensively on large language models, effectively discarding decades of experience in building symbolic or statistical I…
When and Where Did it Happen? An Encoder-Decoder Model to Identify Scenario Context
Enrique Noriega-Atala, Robert Vacareanu, Salena Torres Ashton +3
We introduce a neural architecture finetuned for the task of scenario context generation: The relevant location and time of an event or entity mentioned in text. Contextualizing in…