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
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
Junyi Li, Tianyi Tang, Zheng Gong, Lixin Yang, Zhuohao Yu, Zhipeng Chen, Jingyuan Wang, Wayne Xin Zhao, Ji-Rong Wen
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity?
Subba Reddy Oota, Jashn Arora, Veeral Agarwal, Mounika Marreddy, Manish Gupta, Bapi Raju Surampudi