86 citations · 305 across the 37 of their papers we have counts for
6 papers · 1 filter
Data Contamination Report from the 2024 CONDA Shared Task
Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…
Memorization in In-Context Learning
Shahriar Golchin, Mihai Surdeanu, Steven Bethard +2
In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mecha…
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…
Enhancing Transformer RNNs with Multiple Temporal Perspectives
Razvan-Gabriel Dumitru, Darius Peteleaza, Mihai Surdeanu
We introduce the concept of multiple temporal perspectives, a novel approach applicable to Recurrent Neural Network (RNN) architectures for enhancing their understanding of sequent…