Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
원문 초록
Climate-Eval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. Climate-Eval aggregates existing datasets along with a newly developed news classification dataset, created specifically for this release. This results in a benchmark of 25 tasks based on 13 datasets, covering key aspects of climate discourse, including text classification, question answering, and information extraction. Our benchmark provides a standardized evaluation suite for systematically assessing the performance of large language models (LLMs) on these tasks. Additionally, we conduct an extensive evaluation of open-source LLMs (ranging from 2B to 70B parameters) in both zero-shot and few-shot settings, analyzing their strengths and limitations in the domain of climate change.
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mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks
Making Language Model a Hierarchical Classifier and Generator
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
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