NLP Task
Natural Language Processing (NLP) research currently focuses on enhancing Large Language Models (LLMs) for a wider range of tasks, including improved long-context processing, reliable benchmark creation using synthetic data, and seamless integration of generation and retrieval capabilities. Active research areas involve developing efficient frameworks for handling extensive input sequences within memory constraints, evaluating the effectiveness of LLMs across diverse and challenging benchmarks (including those for specialized domains like finance and law), and mitigating issues like data contamination and hallucination. These advancements are crucial for improving the reliability and applicability of LLMs in various real-world applications, from legal tech to healthcare and beyond.
Papers
Subtle Misogyny Detection and Mitigation: An Expert-Annotated Dataset
Brooklyn Sheppard, Anna Richter, Allison Cohen, Elizabeth Allyn Smith, Tamara Kneese, Carolyne Pelletier, Ioana Baldini, Yue Dong
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, Furong Huang
Divide et Impera: Multi-Transformer Architectures for Complex NLP-Tasks
Solveig Helland, Elena Gavagnin, Alexandre de Spindler
LLM Performance Predictors are good initializers for Architecture Search
Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Dujian Ding
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters
Vipul Rathore, Rajdeep Dhingra, Parag Singla, Mausam
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han