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Found 7 papers, 4 papers with code
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
How do Large Language Models Handle Multilingualism?
We introduce a framework that depicts LLMs' processing of multilingual inputs: In the first several layers, LLMs understand the question, converting multilingual inputs into English to facilitate the task-solving phase.
Exploring the Potential of Large Language Models in Computational Argumentation
As large language models have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks.
Contrastive Chain-of-Thought Prompting
Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning
Instruction tuning has emerged as a crucial process for harnessing the capabilities of large language models (LLMs) by providing explicit task instructions, leading to improved performance in various tasks.
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Zero-Shot Text Classification via Self-Supervised Tuning
In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.
