The increasing complexity of state-of-the-art neural networks creates significant computational energy overhead in Natural Language Processing (NLP) applications. A few studies have explored opportunities to reduce overhead by using a Spiking Neural Network (SNN) instead of a mainstream artificial neural network (ANN) in computer vision and sensory domains. A comprehensive analysis of SNN’s usability for NLP tasks in low-resource languages has yet to be conducted. Hence, in this paper, we present the first-ever comparative energy-overhead analysis of ANNs vs. SNNs for text classification tasks in low-resource Indian languages such as Hindi, Bengali, and Telugu. Our experiments show that although we lose classification accuracy by an acceptable margin when replacing an ANN with an SNN, we save significant energy, which highlights the potential for efficient utilization of edge computing devices to run NLP tasks for low-resource languages. This work shows a direction for advancing energy-efficient NLP applications, especially for low-resource languages. We make code and dataset publicly available [< https://github.com/PS1076/Energy-Accuracy-Frontier>].

One major issue in natural language generation (NLG) models is detecting hallucinations (semantically inaccurate outputs). This study investigates a hallucination detection system designed for three distinct NLG tasks: definition modeling, paraphrase generation, and machine translation. The system uses feedforward neural networks for classification and SentenceTransformer models for similarity scores and sentence embeddings. Even though the SemEval-2024 benchmark shows good results, there is still room for improvement. Promising paths toward improving performance include considering multi-task learning methods, including strategies for handling out-of-domain data minimizing bias, and investigating sophisticated architectures.