3 citations · 8 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
General Purpose Verification for Chain of Thought Prompting
Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou +6
Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information. In this paper, we explore ways to…
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…
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples
Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu +1
We analyze how well pre-trained large language models (e.g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any addit…
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…