Natural Language Inference
Natural Language Inference (NLI) focuses on determining the logical relationship between pairs of sentences, a crucial task for understanding and reasoning with natural language. Current research emphasizes improving NLI model robustness against adversarial attacks and misinformation, enhancing efficiency through techniques like layer pruning and domain adaptation, and developing more reliable evaluation methods that account for human judgment variability and address issues like hallucination in large language models. These advancements are significant for improving the accuracy and trustworthiness of various NLP applications, including question answering, text summarization, and fact verification, ultimately leading to more reliable and explainable AI systems.
Papers
No Strong Feelings One Way or Another: Re-operationalizing Neutrality in Natural Language Inference
Animesh Nighojkar, Antonio Laverghetta, John Licato
Pushing the Limits of ChatGPT on NLP Tasks
Xiaofei Sun, Linfeng Dong, Xiaoya Li, Zhen Wan, Shuhe Wang, Tianwei Zhang, Jiwei Li, Fei Cheng, Lingjuan Lyu, Fei Wu, Guoyin Wang
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data
Janis Goldzycher, Moritz Preisig, Chantal Amrhein, Gerold Schneider
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models
Jiazheng Li, Zhaoyue Sun, Bin Liang, Lin Gui, Yulan He
Sources of Hallucination by Large Language Models on Inference Tasks
Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman
Evaluating and Modeling Attribution for Cross-Lingual Question Answering
Benjamin Muller, John Wieting, Jonathan H. Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Baldini Soares, Roee Aharoni, Jonathan Herzig, Xinyi Wang
Can Large Language Models Capture Dissenting Human Voices?
Noah Lee, Na Min An, James Thorne